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Contextual fuzzy cognitive maps for decision support in geographic information Systems
Abstract
Fuzzy cognitive maps or FCMs have been shown to be useful when
representing qualitative data. We have shown that these FCM structures
can be used to represent quantitative and qualitative data. We
illustrate this structure applied to geographic information system (GIS)
applications. We illustrate the types of CFCMs we can generate using
real census data, human expert knowledge, and quantitative data in the
form of maps in a GIS. The goal of this system is to use objects
(topographical and conceptual) and their relationships, either supplied
by census data or generated by the GIS and to map them as layers in the
GIS. Using fuzzy membership functions from experiments with GIS users,
we can construct CFCMs for decision support. This will also have
significant applications in intelligent servants that are able to assist
and interact with the human user
... This is a hybrid of fuzzy logic and neural networks and can be applied as a "soft computing" method for system modeling. Although adaptable, the method for creating FCMs still heavily depends on human expertise and knowledge [37,[38][39][40]. Many diverse scientific domains have proved that FCMs are useful for showing decision support systems (DSS), including geographic information systems (GIS) [41], expert decision support in applications for urban planning, and medical decision support systems [42,43]. ...
... This is a hybrid of fuzzy logic and neural networks and can be applied as a "soft computing" method for system modeling. Although adaptable, the method for creating FCMs still heavily depends on human expertise and knowledge [37][38][39][40]. Many diverse scientific domains have proved that FCMs are useful for showing decision support systems (DSS), including geographic information systems (GIS) [41], expert decision support in applications for urban planning, and medical decision support systems [42,43]. ...
Globalization has gotten increasingly intense in recent years, necessitating accurate forecasting. Traditional supply chains have evolved into transnational networks that grow with time, becoming more vulnerable. These dangers have the potential to disrupt the flow of goods or several planned actions. For this reason, increased resilience against various types of risks that threaten the viability of an organization is of major importance. One of the ways to determine the magnitude of the risk an organization runs is to measure how popular it is with the buying public. Although risk is impossible to eliminate, effective forecasting and supply chain risk management can help businesses identify, assess, and reduce it. As a result, good supply chain risk management, including forecasting, is critical for every company. To measure the popularity of an organization, there are some discrete values (bounce rate, global ranking, organic traffic, non-branded traffic, branded traffic), known as KPIs. Below are some hypotheses that affect these values and a model for the way in which these values interact with each other. As a result of the research, it is clear how important it is for an organization to increase its popularity, to increase promotion in the shareholder community, and to be in a position to be able to predict its future requirements.
... The choice of the spatial weight matrix has direct impacts on estimation performance, practicability and feasibility of the model (Anselin et al., 1996;Griffith and Lagona, 1998;Griffith, 2010). However, although software implementation in geographic information systems gets many fruitful results and many technologies such as geographical weighted regression and local weighted regression are used to extend the concept of 'neighbour' and 'distance', traditional specifications of W are often too crisp and not quite efficient to reflect the feature of fuzziness even ambiguity of spatial location relationship (Liu and Satur, 1999;Zou and Xiao, 2008;Bordogna et al., 2014). Thus description or creation of is ad hoc (Anselin and Rey, 1991;Griffith and Lagona, 1998) and systematic approach of efficient specification for the spatial weigh matrix is of vital interest. ...
... In view of these traditional specifications, W are often too crisp and not quite efficient to reflect the features of uncertainty, fuzziness even ambiguity of spatial location relationship (Liu and Satur, 1999;Zou and Xiao, 2008;Bordogna et al., 2014). Different weight setting methods play a different role in explaining the specific regional phenomena (Florax et al., 2003). ...
Soft sets are efficient and flexible tools to describe uncertainty and fuzziness. In this paper, we integrate soft set theory into the specification of fuzzy spatial dependent relationship and provide a framework for spatial weight description. We treat the configuration of spatial location relationship as soft sets and propose a new dependence measure based on operations of soft sets. The proposed soft spatial weights matrix efficiently combines information of 'spatial adjacent relation' and 'spatial distance'. Further, Chinese regional industrial agglomeration data are applied to empirical analysis. The spatial autoregressive error panel model (SEM) with our new matrix performs better than that of Moran's I, log-likelihood and interpretation.
... Similarly as most of other predictive models, FCMs can be constructed by domain experts. Numerous approaches of FCMs that refer to multivariate time-series prediction have been applied in several domains such as medicine [3,11,16], geographic information systems [10], economics [7], agriculture management [12]. ...
... For the analysis of results, all FCMs were learned using the fitness function based on the modified calculation of errors (10). It is pinpointed that the previous numerical results in [15] are not comparable to these obtained in this study due to the modification of error function. ...
Fuzzy cognitive maps (FCMs) is a knowledge representation tool that can be exploited for predicting multivariate time-series. FCM model represents dependencies among data variables as a directed, weighted graph of fuzzy sets (concepts).This way,FCM can be easily interpreted or constructed by experts in contrary to black box knowledge representation methods. Since FCM is a parametric model, it can be trained using historical data. So far, the genetic algorithm has been used to solely optimize the weights of FCM leaving the rest of FCM parameters to be adjusted by experts. Previous studies have shown that the genetic algorithm can be also used not only for optimizing the weights but also for optimization of FCM transformation functions. The main idea presented in this chapter is to further extend FCM evolutionary learning process. Special focus is given on fuzzyfication and transformation function optimization, applied in each concept seperately, in order to improve the efficacy of time-series prediction. The proposed extended evolutionary optimization process was evaluated in a number of real medical data gathered from the internal care unit (ICU). Comparing this approach with other known genetic-based learning algorithms, less prediction errors were observed for this dataset.
... The exponential increasing number of fuzzy rules in neuro-fuzzy classifier is one of the difficult problems to be overcome in this paper, such problem is solved by adopting the FCM clustering method [6] for the structure identification in the ANFIS, wish is one of the most widely used neuro-fuzzy models proposed by Jang [2], against diabetes diagnosis problem. The algorithm fuzzy c-means (FCM) is a typical clustering algorithm, which was used in a wide variety of engineering and scientific disciplines such as modeling [7] the decision [8], pattern recognition and classification [9], segmentation [10]. Recently the classification with the approach FCM-ANFIS [11] was applied on database of diabetes collected by the Faculty of Computer Science and Information, University of Technology Malaysia; the results have reached 72.66%. ...
... A hybrid system named ANFIS (Adaptive-Network-Based Fuzzy Inference System or Adaptive Neuro-Fuzzy Inference System) has been proposed by Jang in [8]. The ANFIS is a fuzzy inference system (FIS) based on the model of Takagi-Sugeno (TKS) [14]. ...
Interpretability represents the most important driving force behind the implementation of fuzzy-based classifiers for medical application problems. The expert should be able to understand the classifier and to evaluate its results. The main purposes in this work is the application of a new method based on FCM and ANFIS to diagnose the diabetes diseases by using a reduced number of fuzzy rules with relatively small number of linguistic labels, removing the similarity of the membership functions, preserving the meaning of the linguistic labels (interpretability), and in same time improving the classification performances. Experimental results show that the proposed approach FCM-ANFIS can get high accuracy with fewer rules. On the contrary, by using ANFIS more rules are needed to get a lower accuracy. Moreover the features projected partition in ANFIS is ambiguous and cannot preserve the meaning of the linguistic labels. The best number of the rules is a trade-off between the accuracy and the rules number, also with a minimum of clusters (c=2) and just two fuzzy rules, FCM-ANFIS approach has given the best results with CC = 83.85%, Se = 82.05% and Sp = 84.62% comparing to the other cases.
... Each member of the Resolution-Level must be in the hierarchical order; thus the order is consistently assigned to each member of the Resolution-Level(x) as denoted by the subscript. We use {State, County, TownorCity, Place} as the members of Resolution-Level for our applications, where n is 4, and each member has the resolution order (Liu and Satur 1999). Denote the resolution order of RL (x) as Resolution-Order( RL(x) ) (or RO( RL(x) ) ). ...
... We define four different Resolution-Levels in Section 4.2, including State, County, TownorCity, and Place. It is necessary to create locality layers that correspond to those levels respectively (Liu and Satur 1999). Three locality layers are created: COUNTY for the Resolution-Level County, PLACE_PL for TownorCity, and PLACE_PT for Place (Layer names are capitalized to tell them apart from other names). ...
It is only recently that the fuzzy-set theoretic approach to spatial objects and their concepts has joined the mainstream of geographic information system (GIS) and science (GIScience). Several reasons account for this. Recent research on spatial objects has revealed that spatial vagueness is inherent in some geographic features (Burrough and Frank 1996). For instance, the boundary between a mountain and a valley is not sharply defined. Furthermore, even if a geographic phenomenon is best described as crisp, humans tend not to reason in a precise manner, but rather in an approximate manner (e.g. they live near Chicago). Moreover, perception and cognition vary widely between individuals. Furthermore, information can be incomplete or imprecise due to rough measurements or to our incomplete ability to grasp the scope and detail of spatial objects. In other words, there always exists a gap between the reality and its representation. We use fuzzy set theory (Zadeh 1965) as a mean to reconcile discrepancies existing between reality and its representation. We discern three representational different levels at which fuzzy set concepts can be applied, namely the ontology, perception, and implementation levels. The (spatial) ontology level pertains to generic concepts inherent in spatial objects. The perception level concerns the mental models used to perceive the environment. The implementation level encompasses the errors that have propagated during system implementation. The combination of spatial vagueness, diverse human perceptions, and implementation errors account for the gap existing between reality and its representation. In general, fuzzy set concepts preserve details (Robinson 2002) whereas traditional (crisp) GIS data models overlook the loss of information by forcing reality into a coarse (in the sense of low resolution) representation. Fuzzy set theory can overcome the gap by providing mechanisms for ontologically and cognitively plausible (Worboys 2001) and error-sensitive (Duckham et al. 2001) representation of the reality. In sum, fuzzy set theory provides a means to address various kinds of uncertainty such as spatial vagueness, human perception, and imperfect information. This study is part of a larger project aimed at geographically referencing the fatal accident data. Our task is to pinpoint the location where a traffic crash is most likely to have occurred given the limited and imprecise information available on this crash. In our study, georeferencing can be roughly defined as the conversion of the linguistic description of a location to a quantitative specification. As Goodchild (2000) pointed out, effective georeferencing can be a matter of life and death in the case of communication between a caller and an emergency dispatcher. The linguistic description of location is sometimes not clear-cut, not only because many alternate names are used to refer to the same location, but also because the location itself is not well defined. We focus on the problem of determining the location of a certain locality. We hypothesize that location indeterminacy of localities is caused by spatial vagueness, interpersonal differences in perception, and imperfect information. We compute the value that quantifies location indeterminacy by modeling the indeterminate part of localities by a fuzzy set membership function. We examine the relationship between the value of location indeterminacy and attributes of localities in order to test the stated hypotheses. The purpose of this research is to show how fuzzy set theory can be properly applied in modeling localities. Also the result will assure whether there exists fuzziness in determining the location of locality. This study develops a fuzzy set membership function for indeterminate boundaries of localities. By testing our hypotheses on the relationship between location determinacy and characteristics of locality, we examine whether fuzzy set theories can capture various kinds of uncertainty at the ontology, perception, and implementation levels. Modeling localities by fuzzy sets has a definite advantage over a crisp set in that it makes best possible use of sparse information to reconstitute detail. More specifically, fuzzy-set-based localities constitute a closer depiction of reality, such as overlapping memberships of localities. Next, fuzzy set provides a conservative representation tool for individual differences in the perception. Finally, allowing the soft processing (fuzzy set modeling) over the hard data (reference data) can minimize the problems caused by the imperfection of source data. The remainder of this chapter is organized as follows. In Section 4.2, the specific georeferencing motivating this study is described. We formulate research hypotheses and specify the assumptions on which this study is based. In Section 4.3, we give a brief overview of related research, such as the ontology of spatial objects, the representation of fuzzy regions, and the notion of nearness. In Section 4.4, we define the fuzzy set membership function of localities. The implementation steps in GIS are described in Section 4.5. The analyses of results are given in Section 4.6. We examine if fuzziness is substantial in identifying localities. By looking at the cases that are georeferenced by a fuzzy set modeling, we may or may not find evidence of fuzziness in locality. The hypothesis is examined also. Finally, Section 4.7 concludes this study.
... Over the last years, a variety of FCMs have been used for capturing -representing knowledge and intelligent information in engineering applications, for instance, GIS (Liu & Satur, 1999) and fault detection (e.g. Ndouse & Okuda, 1996;Pelaez & Bowles, 1995). ...
... For example, the research in Silva (1995) proposed new forms of combined matrices for FCMs, the research in Hagiwara (1992) extended FCMs by permitting non-linear and time delay on the arcs, the research in Schneider, Schnaider, Kandel, and Chew (1995) presented a method for automatically constructing FCMs. More recently, Liu and Satur (1999) have carried extensive research on FCMs investigating inference properties of FCMs, proposed contextual FCMs based on the object-oriented paradigm of decision support and applied contextual FCMs to geographical information systems (Liu, 2000). ...
... Over the last years, a variety of FCMs have been used for capturing -representing knowledge and intelligent information in engineering applications, for instance, GIS (Liu & Satur, 1999) and fault detection (e.g. Ndouse & Okuda, 1996;Pelaez & Bowles, 1995). ...
... For example, the research in Silva (1995) proposed new forms of combined matrices for FCMs, the research in Hagiwara (1992) extended FCMs by permitting non-linear and time delay on the arcs, the research in Schneider, Schnaider, Kandel, and Chew (1995) presented a method for automatically constructing FCMs. More recently, Liu and Satur (1999) has carried extensive research on FCMs investigating inference properties of FCMs, proposed contextual FCMs based on the object-oriented paradigm of decision support and applied contextual FCMs to geographical information systems (Liu, 2000). ...
This paper presents the application of a fuzzy cognitive map (FCM) based theoretical framework and its associated modeling and simulation tool to strategy maps (SMs). Existing limitations of SMs are presented in a literature survey. The need for scenario based SMs with inherited ability to change scenarios dynamically as well as the missing element of time are highlighted and discussed upon. FCMs are presented as an alternative to overcome these shortfalls with the introduction of fuzziness in their weights and the robust calculation mechanism. An FCM tool is presented that allows simulation of SMs as well as interconnection of nodes (performance measures) in different SMs which enables the creation of SM hierarchies. An augmented FCM calculation mechanism that allows this type of interlinking is also presented. The resulting methodology and tool are applied to two Banks and the results of these case studies are presented.
... They have also been used in the presentation of social scientific knowledge and description in various decision-making methods (Zhang et al. 1989(Zhang et al. , 1992Georgopoulos et al. 2003). Other notable applications include geographical information systems (Liu and Satur 1999;Satur and Liu 1999b, a), pattern-recognition applications (Papakostas et al. 2006(Papakostas et al. , 2008, numerical and linguistic prediction of time-series functions (Silva 1995;Stach et al. 2008), technological (Stylios and Groumpos 2004), industrial (Abbaspour Onari and Jahangoshai Rezaee 2020; Markaki and Askounis 2021) and medical applications (Froelich et al. 2012;Amirkhani et al. 2017Amirkhani et al. , 2018Apostolopoulos et al. 2017;Bevilacqua et al. 2018;Puerto et al. 2019). ...
Fuzzy cognitive maps (FCM) have recently gained ground in many engineering applications, mainly because they allow stakeholder engagement in reduced-form complex systems representation and modelling. They provide a pictorial form of systems, consisting of nodes (concepts) and node interconnections (weights), and perform system simulations for various input combinations. Due to their simplicity and quasi-quantitative nature, they can be easily used with and by non-experts. However, these features come with the price of ambiguity in output: recent literature indicates that changes in selected FCM parameters yield considerably different outcomes. Furthermore, it is not a priori known whether an FCM simulation would reach a fixed, unique final state (fixed point). There are cases where infinite, chaotic, or cyclic behaviour (non-convergence) hinders the inference process, and literature shows that the primary culprit lies in a parameter determining the steepness of the most common transfer functions, which determine the state vector of the system during FCM simulations. To address ambiguity in FCM outcomes, we propose a certain range for the value of this parameter, λ, which is dependent on the FCM layout, for the case of the log-sigmoid and hyperbolic tangent transfer functions. The analysis of this paper is illustrated through a novel software application, In-Cognitive, which allows non-experts to define the FCM layout via a Graphical User Interface and then perform FCM simulations given various inputs. The proposed methodology and developed software are validated against a real-world energy policy-related problem in Greece, drawn from the literature.
... It has the characteristics of intuitiveness, vividness, ambiguity, and fragmentation. The result is not necessarily correct, but it is the most important part of the environment subjectively considered by the individual [20][21][22][23]. In addition, in the same spatial environment, different individuals will have different cognitive maps, depending on their personality, age, occupation, social status, lifestyle, and other characteristics. ...
The fundamental purpose of future urban development is to meet residents’ yearning for a better city life with the rapid development of urbanization. This study uses a multinomial logit model and cognitive map to evaluate residents’ spatial image perception of urban green space. A field study and data collection were conducted from July to August 2019, using the typical urban green space area in Beijing as the research object. Based on 375 valid questionnaires and 139 cognitive maps, the study analyzed and evaluated the image characteristics and differences of residents to the urban green space under different conditions. The results show the following. First, there is a close relationship between residents’ preference and the characteristics of urban green spaces, especially the working and living environment and characteristics will have a great influence on it. Second, the cognitive map drawn by Beijing residents can be divided into sequential and spatial cognitive maps, and the image perception shows diversified characteristics. However, the perception is relatively superficial overall, and most are simple line maps. Besides, according to the analysis of the elements of the cognitive map, the advantages and disadvantages of each type of urban green space are closely related to their geographical location and internal structure. This study has two key findings. First, the construction of urban green spaces in various cities should be carried out according to local conditions, considering the scientific basis and reasonableness of urban green space in terms of structural setting. Second, the multinomial logit model and cognitive map can effectively quantify the subjective evaluation of respondents’ spatial perceptions in a relatively simple manner, which can be further expanded in the application system design of the method.
... Although the methodology of creating FCMs is easily adjustable, it is heavily based on human knowledge and expertise [77]. FCMs have proven useful in illustrating decision support systems in many different scientific sectors like medical decision support systems [78], implementing expert decision support in urban design areas [79], and geographical information systems (GIS) [80]. Concurrently with the proliferation of Internet applications and e-commerce in business, firms are geared towards adopting web technologies that will enhance their strategic decision-making abilities [81]. ...
The ongoing COVID-19 pandemic has proven to be a real challenge for courier companies on a global scale and has affected customer behavior worldwide. This paper attempts to propound a new methodology in order to predict the effect of courier companies’ e-commerce on customers’ risk perception regarding their online behavior after the outbreak, and the final effect of their behavior on the global ranking of the company’s website, utilizing passive crowdsourcing data from five world-leading courier companies as representative examples of their respective business sectors. The results will allow supply chain risk management (SCRM) managers to make effective strategic decisions regarding the efficient allocation of resources to mitigate the corporate risk to their organization during a novel crisis. In our paper, we monitored five key performance indicators (KPIs) over a 24-month period (March 2019–February 2021) as the first of a suggested three-level analysis process using statistical analysis and fuzzy cognitive mapping techniques. We propose that courier service companies should manage the risk of a potential novel crisis by improving the reputation and brand name of the company, since customers tend to trust an established brand.
... In this paper, a FCM is formally defined as an ordered pair M = (C, A), where C is a finite set of cardinality |C| = n , whose elements are called concepts, and A is a matrix of type n × n with values in the real interval <0, 1> (alternatively, in <−1, 1>). Elements of matrix A are interpreted as the levels of causal relations between pairs of concepts in C. Further, we shall consider an evaluation vector of the fuzzy cognitive map M, defined as a mapping e: C <0, 1> and its values are interpreted as activation levels of concepts in C. Decision support, and model behavior prediction as well, represent the most often cited domains of FCMs utilization, see (Khan, Chong & Gedeon, 2000), (Liu, & Satur, 1999), (Salmeron, 2009). FCMs as a supporting tool for decision making process were considered in (Gavalec, & Mls, 2003). ...
... Furthermore, the capabilities of this method, such as the ability to model complex systems with limited and missing data or in the situation in which the data collection process is expensive, has increased its application domain [103]. Although this subject has been introduced in the field of psychology, its applications have been developed in other disciplines such as geography [87], education [37], systems control [51,52], transportation [18], medicine [10], supply chain [6], banking [17], engineering [22], energy [5], and environment [117]. ...
Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.
... Cognitive maps use causal links to represent concepts and it was used to represent the decision-making process of different team members in new product design (Carbonara & Scozzi, 2006). Fuzzy cognitive maps incorporate fuzziness involved in the relationships between concepts and were used to capture the causal reasoning process in geographic information system design (Liu & Satur, 1999). ...
Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.
... There is a wide range of proposals focused on FCM in the academic literature. Some proposals consider: Emphasize its links to artificial neural network architecture [38], the processing of knowledge [39], the idea of virtual worlds [40], the implications of time-based relations [41], the emergence of software tools like FCModeler [42], the representation of hyperknowledge in strategic formulation processes [43], the use of FCM in control processes of distributed systems [44], the use of FCM in decision-making based on Geographic Information Systems (GIS) [45], applications based on FCM focused on control systems [46] and many more authors. ...
... w 86 is The weight of the interconnection from concept C j to concept C i and f is a threshold function. The unipolar sigmoid function is the most used threshold function, (Liu and Satur, 1999) where λ>0 determines the steepness of the continuous function f . The sigmoid function ensures that the calculated value of each concept will belong to the interval [0,1]. ...
... More specifically, it is used when the causality between concepts cannot be described precisely owing to high complexity or data unavailability. This holds true for many fields of application, ranging from traditional ecology and environmental management to engineering and information systems [3], [4]. In business domain, the applications include, among others, the design of balanced scorecards [5], performance management [6], knowledge management modeling [7], supplier selection [8], customer relationship management [9], project management [10] or production management [11]. ...
... Hong and Han (2002) and Lee et al. (2002) combined FCMs with data mining techniques to use expert knowledge. By investigating inference properties of FCMs, Liu and Satur (1999) proposed contextual FCMs, which introduce the object-oriented paradigm for decision support systems. Liu (2000) also used the contextual FCMs for geographical information systems. ...
In this paper, a new fuzzy cognitive mapping approach is proposed, in which the values of the concepts and the strength of the links are presented through a set of possible values under Hesitant Fuzzy Sets (HFSs). In particular, we discuss student accommodation problems which can occur at every education system and affect students’ academic achievements. In this regard, we interview with the university chancellor, the student accommodation manager, and the top student to create individual cognitive maps; then, these cognitive maps aggregate to build the strategic cognitive map. In this study, based on the experts’ opinions, three kinds of scenarios, optimistic, moderate, and pessimistic, are developed. By so doing, the concept with the most value is determined. Furthermore, the effects of different initial values of the concepts on the final values and the number of simulation iterations are analyzed.
... However, G(A) − G(A ) according to (16) equals ...
In this chapter, we present a study for the existence of equilibrium points of FCNs equipped with continuous differentiable sigmoid functions that have contractive or at least nonexpansive properties. The study is done by using an appropriately defined contraction mapping theorem and the nonexpansive mapping theorem. It is proved that, when the weight interconnections fulfill certain conditions, related to the size of the FCN and the inclination of the sigmoid functions, the concept values will converge to a unique solution regardless of their initial states, or in some cases a solution exists that may not necessarily be unique.
Otherwise the existence or the uniqueness of equilibria may or may not exist, it may depend on the initial states, but it cannot be assured. In case the FCN has also input nodes (that is nodes that influence but are not influenced by other nodes), the unique equilibrium does not depend solely on the weight set, as in the case of FCNs with no input nodes; it depends also on the values of the input nodes. Numerical examples explore the results and a thorough discussion interprets them.
... The easy of use and the low time requirement are important features of FCMs. FCMs have been widely applied in computing and decision sciences [6,16,22,30,36373845] , although other research areas have used these techniques, such as business and management [?], political decisions [42], agriculture and ecological sciences [25,35] , engineer- ing [?,21], robotics [44], pattern recognition [33] and medicine [8,11,2728293039]. The main goal of this work is to present a method based on fuzzy inference map approach that can be applied for the development of an expert system for predicting the risk of pulmonary infection. ...
In this work, the Fuzzy Inference Map approach also known as Fuzzy Cognitive Map is investigated to handle with the problem of risk analysis and assessment of pulmonary infections during the patient admission into the hospital. A Fuzzy Inference Mapping is an artificial cognitive structure within which the relations between the elements of a mental landscape can be used to assess the impact of these elements. It has the advantageous features of representing medical knowledge in a symbolic manner, giving system's transparency, interpretability of results and easiness of use by non experts. Fuzzy Cognitive Map FCM proved by the literature as an appropriate reasoning tool to explicitly encode the knowledge and experience accumulated on the operation of a complex system. This study presents a first tool for making decisions in medical domain that will help physicians, through the design of the knowledge representation and reasoning using FCM to automate the decision making process in the case of infectious diseases prediction. After drawing the FCM model for pulmonary risk prediction, the Decision Making Trial and Evaluation Laboratory DEMATEL method is implemented to analyze the map and outrank the concepts according to their importance for physicians. A number of different scenarios concentrated on the pulmonary infections are examined to demonstrate the application of the proposed methodology and its prediction capabilities. This work proves that FCM can handle efficiently with uncertainty in modeling medical knowledge.
... FCM is able to clearly show the structural relationships in built systems. In particular, it makes full use of prior knowledge to support the system's adaptive behavior [22]. ...
Designer thinking has received much attention as an innovative problem-solving approach. However, it is also a vague and unclear activity which can only be studied through the input and output results in a design process. This paper proposes that the visual design state is the external performance of design thinking, which is used to clarify vague design thinking. In this paper, a method for expressing and capturing design states based on multi-fuzzy cognitive mapping is presented. First, an agent model is built to capture the design states, and the weight values of the multi-fuzzy cognitive map matrix are calculated. The tightness of the design state route is then deduced. An example is presented to demonstrate the use of this method, which is able to support the designer with the necessary design resources and an intelligent design environment.
... w ji is The weight of the interconnection from concept Cj to concept Ci and f is a threshold function. The unipolar sigmoid function is the most used threshold function, (Liu and Satur, 1999) where λ>0 determines the steepness of the continuous function f . The sigmoid function ensures that the calculated value of each concept will belong to the interval [0,1]. ...
... Fuzzy cognitive maps introduce fuzzy degrees of interrelationships between concepts (Miao and Liu, 2000). They have been widely used to capture the causal reasoning process present in geographic information systems (Liu and Satur, 1999), urban design (Xirogiannis et al., 2004), and fault detection and troubleshooting systems (Perusich, 2008), and the like. ...
As industry sectors mature, a critical challenge confronting many companies is how to provide users with engaging experiences within a product ecosystem (e.g., a subway station ecosystem). User experience goes far beyond cognitive aspects to encompass affective aspects, where the former accommodates users’ cognitive capabilities, tendencies, and limitations while the latter concerns how to elicit desirable emotional responses from users. Both aspects have major impacts on each other and importantly influence user satisfaction.
Although companies have long been concerned with users in the product design and development process, the emphasis has been on the cognitive aspects to support users’ cognitive processes through human-product interactions, demonstrated as usability studies in the areas of human factors and human-computer interaction. Further, traditional usability studies often consider interactions between the user and a single object (product), ignoring the kind of user interactions in a context of product ecosystems that consist of multiple interdependent products, users, and ambient factors (e.g., environmental settings and cultural factors). On the other hand, user pleasure has been considered in the new development of human factors and ergonomics; affective human-computer interaction is also gradually being recognized. From a holistic perspective, it is imperative to study user experience design incorporating its two essential dimensions, i.e., the affective dimension and the cognitive dimension, in the context of product ecosystems.
This research develops a conceptual model that elaborates the operational mechanism of key factors underlying product ecosystems for user experience design while leveraging both affective and cognitive dimensions of users. In addition, a technical framework is proposed to drive product ecosystem design for user experience with rigorous engineering methods. The technical framework has three consecutive and iterative steps, i.e., affective-cognitive need elicitation, affective-cognitive analysis, and affective-cognitive fulfillment.
... , A(D − 1) of a given system. The FCM model represented by σ uses each A(t), with t = 0, ..., D − 2, as an initial state vector to compute a vec-torÂ(t + 1) by applying the inference engine described by (2). The collection of vectorsÂ(t) are successively compared with A(t) to assess the quality of the FCM model represented by σ. ...
Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models.
... FCMs describe expert knowledge of complex systems with high dimensions and a variety of factors. An increased interest about the theory and application of FCMs in complex systems has been also noted, and their validity and usefulness has been proved in the various fields (Eden & Ackermann, 1989; Eden, Jones, & Sims, 1979; Kwahk & Kim, 1999; Lee & Kwon, 2006; Lee & Kwon, 2008; Liu & Satur, 1999; Nelson, Nadkarni, Narayanan, & Ghods, 2000; Satur & Liu, 1999; Zhang, Chen, & Bezdek, 1989; Zhang, Wang, & King, 1994). On the other hand, the usefulness of FCMs inspires many researchers and practitioners in various fields to construct their own FCM, and it is common that they have created myriads of similar cognitive maps again and again despite of the existence of similar FCMs. ...
... FCM has shown to be useful in modeling complex dynamic systems. Some reported applications are: stock investment analysis [20], decision support in geographic information systems [21], human relationship management in airline service [22], and decision support in medicine [23]. However, most of these reports mainly focus on the modeling of some application domains, and they avoid touching on the practical issues related to the efficient reasoning about the represented causal knowledge. ...
Managing design knowledge is an important concern for industry, including engineering. Engineering firms are facing pressures to increase the quality of their products, to have even shorter lead times and reduced costs. There is also a trend towards globalization resulting in complex supply chains and the need to manage teams that are not necessarily co-located. Design knowledge needs to be exchanged and accessed efficiently. Other motivations for managing design knowledge are to provide a trail for product liability legislation and to retain design knowledge and experience as engineering designers retire. Fuzzy Cognitive Map (FCM) is one of the main formalisms for modeling, representing and reasoning about causal knowledge. Despite the fact that FCM has been used extensively in causal knowledge engineering, there is a lack of methodology for the systematic construction of FCM. Although some techniques were used in the individual construction processes, these techniques were either not systematically documented or too specific to the problem at hand. FCM and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal design knowledge. Despite their extensive use in causal design knowledge engineering, there is no reported work which compares their respective roles. This paper deals with three topics, which are systematic constructing FCM, a methodology for FCM-BBN conversion, and comparison FCM and BBN. BBN has a sound mathematical foundation and reasoning capabilities, also it has an efficient evidence propagation mechanism and a proven track record in industryscale applications. However, BBN is less friendly and flexible, and often very time-consuming to generate appropriate conditional probabilities. Thus, Fuzzy Cognitive Map (FCM) is used for the indirect knowledge acquisition, and the causal knowledge in FCM is systematically converted to BBN. Finally, we compare BBNs directly generated by domain experts and generated from FCM, with a realistic industrial example, a fuel nozzle for an aerospace engine.
... Os mapas cognitivos têm se destacado, juntamente com a lógica difusa, em diversos domínios de aplicação como em controle e supervisão de processos, sistemas multiagentes, dinâmicos e apoio à decisão [3,4]. Nos mapas cognitivos difusos, propostos primeiramente por Kosko [5,6], cada nó do mapa é um conjunto difuso e a relação entre dois nodos é definida como a proporção com que cada conjunto contém o outro. ...
Este artigo apresenta a implementação de um sistema inteligente de apoio a avaliação do desempenho técnico e estratégico de subestações de uma companhia de energia elétrica considerando-se aspectos como continuidade, confiabilidade, operação, atendimento e manutenção. Estrutura-se o problema em um mapa (modelo) cognitivo através da representação do discurso e experiência adquirida de um especialista com respeito ao contexto decisório. Em adição, desenvolve-se uma formulação alternativa para inferência em mapas cognitivos por meio da concepção de uma lógica 4-valorada intervalar, em extensão à lógica difusa tradicional. A partir do estabelecimento de regras de inferência no modelo estruturado, o programa computacional desenvolvido é capaz de fornecer uma hierarquização qualitativa das subestações assim como obter e monitorar, nesta hierarquização, o impacto de ações potenciais como na alocação de transformadores, configuração, radialidade, equipamentos, atendimento a consumidores especiais, proteção e automação, quantidade de alimentadores, energia consumida e número de consumidores atendidos. Utilizou-se neste projeto, da experiência e capacitação de um especialista e dados com informações de rede elétrica, equipamentos, confiabilidade e atendimento de subestações de distribuição da Companhia Paranaense de Energia. O sistema computacional pode atuar em estudos de redes de T&D, alocação de recursos, treinamento e apoio ao planejamento estratégico das subestações.
... Application areas on which the FCMs have demonstrated an exceptional performance include decision support [4], process control [7], pattern recognition [8], and data mining systems [9]. They have been applied on various domains such as biomedicine [10], [11], geographic information systems [11], [12], and time-series analysis [9], [13]. ...
Uncertainty and imprecision characterize human cognitive and reasoning processes. Fuzzy cognitive maps (FCMs) are computationally simple yet effective structures to approximately model and simulate such processes. A limitation of current FCMs is that they are unable to model the hesitancy introduced into a complex system due to imperfect facts, missing information, and indecision. To cope with this issue, we propose a novel extension of the FCM model which is based on the theory of intuitionistic fuzzy sets. This intuitionistic FCM (iFCM) model, which is denoted as iFCM-II, inherently exploits the mathematical framework of intuitionistic fuzzy sets for the definition of the concepts constituting the cognitive map and their interrelations, as well as for reasoning. Furthermore, unlike the previous iFCM model, which is denoted as iFCM-I, it enables an intuitionistic estimation of hesitancy at the output concepts, thus offering a natural mechanism to assess the quality of its output. The advantages of the proposed iFCM model over the current FCM and iFCM models are demonstrated with reproducible numeric examples for process control and decision support applications.
... Fuzzy cognitive maps introduce fuzzy degrees of interrelationships between concepts (Miao and Liu 2000). They have been widely used to capture the causal reasoning process present in geographic information systems (Liu and Satur 1999), urban design (Xirogiannis et al. 2004), and fault detection and troubleshooting systems (Perusich 2008), and the like. ...
The prevailing practice of design for mass customization manifests itself through a configure-to-order paradigm, which means to satisfy explicit customer needs (CNs) and built upon legacy design. With pervasive connectivity and interactivity of the Internet and sensor networks, personalization has been witnessed in a number of industry sectors as a promising strategy that makes the market of one a reality. Mass personalization entails a strategy of producing goods and services to satisfy individual customer’s latent needs with values outperforming costs for both customers and producers. This review paper envisions an affective and cognitive design perspective to mass personalization. By exploiting implicit market demand information and revealing latent CNs, mass personalization aspires to assist customers in making better informed decisions, and to the largest extent, to anticipate customer satisfaction and adapt to customer delight. The key dimensions of mass personalization are identified and discussed. By capitalizing on user experience, affective and cognitive design for mass personalization is expected to address individual customer’s latent CNs. The decisions of affective and cognitive design, involving affective and cognitive needs elicitation, affective and cognitive analysis, and affective and cognitive fulfillment, are reviewed with a wide range of interests, including engineering design, human factors and ergonomics, engineering psychology, marketing, and human-computer interaction. Recent trends and future research directions are also speculated to inspire more meaningful research in this area.
Cities are increasingly looking to become smarter and more resilient. Also, the use of computer vision takes a considerable place in the panoply of techniques and algorithms necessary for the 3D reconstruction of urban built environments. The models thus obtained make it possible to feed the logic of decision support and urban services thanks to the integration of augmented reality. This chapter describes and uses Fuzzy Cognitive Maps (FCM) as computing framework of visual features matching in augmented urban built environment modeling process. It is a combination of the achievements of the theory of fuzzy subsets and photogrammetry according to an algorithmic approach associated with the ARKit renderer. In this experimental research work, part of which is published in this chapter, the study area was confined to a portion of a housing estate and the data acquisition tools are in the domain of the public. The aim is the deployment of the algorithmic process to capture urban environments built in an augmented reality model and compute visual feature in stereovision within FCM framework. The comparison of the results obtained with our approach to two other well-known ones in the field, denotes the increased precision gain with a scalability factor.
The concept of emotion is closely related to affect, which is an encompassing term, consisting of emotions, feelings, moods, and evaluations. Organizations, conferences, and special issues related to emotion and design in human factors and ergonomics have been burgeoning. Core affect is object-free without being directed at anything, that is, no emotional associations, whereas affective quality is related to or belongs to the product and has the ability to cause a change in core affect during the human-product interaction process so that the product is attributed with creating emotional associations. Quality function deployment is a method that first transforms qualitative customer needs into quantitative parameters, then deploys the functions to form product quality, and then translates product quality into design elements, and finally to specific manufacturing processes. Emotional design has been well recognized in the domain of human factors and ergonomics. The chapter reviews related models and methods of emotional design.
In this study, the fuzzy causal map inference mechanisms are analyzed for decision making tasks and a comparative analysis is performed to handle with the uncertainty in the problem of pulmonary risk prediction. Fuzzy Cognitive Mapping (FCM) is a causal graphical representation including nodes, determining the most relevant factors of a complex system, and links between these nodes determining the relationships between those factors. It represents knowledge in a symbolic manner and relates states, processes, policies, events, values, and inputs in an analogous manner. In the proposed work, a modified inference mechanism for FCM approach, which handles uncertainty and missing data, is presented and compared with the common fuzzy causal graph reasoning process for a medical diagnosis problem. Through this study, we overcome the problem of missing data and/or incomplete knowledge, especially for the cases where there is no any information about a concept-state or the knowledge of some concepts is insufficient. By this way, the rescaled inference process is proved more reliable, yielding more exact and natural inference results than traditional FCMs. A number of different scenarios for medical diagnosis concentrated on the pulmonary infections are elaborated to demonstrate the functioning of the rescaled FCM inference mechanism.
The literature on Futures Studies shows the need to give the process of inference of the future, guidelines that help to face turbulence and recognize the emerging properties that belong to the dynamics of complex social systems. These systems are always in a state of non-equilibrium. Therefore, from a prospective point of view, a system must have means to monitor and understand the changes that occur in its environment, which in many cases express mega trends, often in conflict with each other. To respond to the challenges, the proposal called Meta-Prospective allows to combine Soft Computing with prospective strategic methods, providing the opportunity to develop strategic intelligence capabilities based on prospective thinking and modeling, but prioritizing the process on the methods to turns the proposal into a humanized model. This chapter develops the proposed called Meta-Prospective.
Terrorism and particularly suicide terrorist campaigns have became a high priority for governments, the media, and the general public. It is imperative to have a comprehensive security risk management programme including effective risk assessment and appropriate decision support for such activities. Terrorism risk assessment (TRA) therefore plays a crucial role in national and international security. In order to predict terrorist behaviour from a given set of evidence (including hypothesised scenarios), it is often necessary for investigators to reconstruct the possible scenarios that may have taken place.
This paper analyzes rural tourism development in Jiangsu province of China by using a cognitive mapping approach. The cognitive maps were elicited by nine focus group interviews in different regions of Jiangsu province. Cognitive maps were analyzed by using Decision Explorer’s functions for domain analysis, loop analysis, and cluster analysis. The results indicate different key issues and internal structure of rural tourism system in northern and southern region of Jiangsu province. However, current land use regulation is the common bottleneck to limit rural tourism development for Jiangsu province.
The general research methodology we have applied includes both qualitative method and quantitative method, which is used to verify if teachers and students are satisfied with our research work as well as to verify if our research work can provide with better teaching approaches.
Fuzzy Cognitive Networks (FCN) stem from Fuzzy Cognitive Maps (FCM), initially introduced by Kosko to model complex behavioral systems in various scientific areas. This chapter presents basic definitions related to FCM and the traditional way of their operation. It starts with a brief bibliographical introduction, presenting various extensions of the initial model and areas of application. Since their convergence is a crucial point for the development of Fuzzy Cognitive Networks, particular emphasis is given to the subject of their convergence to equilibrium points and some noticeable peculiarities that may appear. Then, the scope of Part II of this book is analyzed and the organization of the relevant chapters is presented.
This paper presents the application of a Fuzzy Cognitive Map (FCM) based theoretical framework and its associated modeling and simulation tool to StrategyMaps (SMs). Existing limitations of SMs are presented in a literature survey. The need for scenario based SMs with inherited ability to change scenarios dynamically as well as the missing element of time are highlighted and discussed upon. FCMs are presented as an alternative to overcome these shortfalls with the introduction of fuzziness in their weights and the robust calculation mechanism. An FCM tool is presented that allows simulation of SMs as well as interconnection of nodes (performance measures) in different SMs which enable the creation of SM hierarchies. An augmented FCM calculation mechanism that allows this type of interlinking is also presented. The resulting methodology and tool are applied to two Banks and the results of these case studies are presented.
Je popsána metodika výzkumu mentálních a kognitivních map, dosažené výsledky a další směry výzkumu této problematiky.
*********cite as: Zelenka, J., Mls, K., Šípek, J., Štyrský, J., Bodnárová, A., Pásková, M., Gavalec, M., Lehmannová, Z., Ježek, B., Vaněk, J., Janečka, P., Vydra, L., Poděbradský, P., Pilařová, Z., Franěk, M. Výzkum kognitivních a mentálních map. Hradec Králové : Univerzita Hradec Králové, 2008. 197s. ISBN: 978-80-7041-323-4
Fuzzy Cognitive Map, one of ways to model, describe and infer reasoning relations, is widely used in the field of reasoning knowledge engineering. Despite of the natural and easy understanding of decision and smooth explanation of relation between front and rear, reasoning relation is organized with mathematical haziness and complex algorithm and rarely has an interactive user interface. This paper suggests an interactive Fuzzy Cognitive Map(FCM) construction support system. It builds a FCM increasingly concerning multiple experts` knowledge. Futhermore, it supports user-supportive environment by dynamically displaying the structure of Fuzzy Cognitive Map which is constructed by the interaction between experts and the system.
Fuzzy cognitive map (FCM) [5] was a modification of the cognitive map of Axelrod [1]. FCMs can be used in knowledge representation and inference which are essential to any intelligent system. FCM encodes rules in its networked structure in which all concepts are causally connected. Rules are fired based on a given set of initial conditions and the structure of the FCM. The resulting map pattern represents the causal inference of the FCM. In FCMs, we are able to represent all concepts and arcs (edges) connecting the concepts by symbols or numerical values. Moreover, in such a framework it is possible to handle different types of uncertainties effectively and to combine readily several FCMs into a single FCM that takes the knowledge from different experts into consideration [6]. FCM provides a mechanism for handling causality between events/objects in a more natural fashion. Indeed, FCM is a flexible and realistic representation scheme for dealing with knowledge. This scheme is potentially useful in the development of human-centered systems that require soft-knowledge in the sense that system concepts, their relationships, and the meta-system knowledge can be represented only to a certain degree. In addition, subtle (spatial and temporal) variations in the knowledge base can often result in completely different outcomes or decisions [25]. Many recently developed systems and successful applications have shown that fuzzy cognitive maps represent a promising paradigm for the development of functional intelligent systems [10, 15, 19, 20].
Fuzzy Cognitive Map (FCM) is a powerful and flexible framework for knowledge representation and causal inference. However, in most real applications, it is difficult to design and analyze FCMs due to their structural complexity. Simplification, merging, and division are the important operations on the structure of FCMs. In this paper we present approaches to simplifying FCMs. These approaches show how to clean up a FCM, how to divide a complex FCM into basic FCMs, and how to extract the eigen structure of these basic FCMs. Two improved methods for merging FCMs from different human experts are also proposed in this paper. We discuss difficulties in merging FCMs and present possible solutions.
Fractional Bessel processes are defined and considering the processes associated with fractional Bessel processes XH(t)=∫0t sign(BH(s))dBH(t),1/2<H<1 ...
Large scale systems (LSS) have been traditionally characterized by a large number of variables, nonlinearities and uncertainties. Their decomposition into smaller, more manageable subsystems, possibly organized in a hierarchical structure, has been associated with intense and time – critical information exchange and with the need for efficient and coordination mechanisms. A critical overview of the different theories and algorithms for LSS is provided. The issue of system complexity has become transparent. As the complexity of such systems increase and the presence of uncertainties play a role on the performance of LSS and HMS, new system theoretic methods become more crucial and are urgently needed. Intelligent Systems (IS) and Fuzzy Cognitive Maps (FCM) theories are such new theoretic approaches in modeling Large Scale Dynamic Complex Systems (LSDCS). An FCM is based on fuzzy logic and Neural Networks. FCM integrates the accumulated experience and knowledge on the operation of the system, as a result of the methods by which it is constructed. The new theories of FCMs are reviewed and used to model LSS and Dynamical Hierarchical Control Systems. A number of applications in using FCM to model complex systems from industrial processes economics, energy, environment, health international relations and political developments are mentioned. New challenges and research opportunities are presented and discussed.
A learning methodology is proposed for automatically constructing fuzzy cognitive map. In the proposed method, the evolutionary mechanism of cellular automata is used to learn the connection matrix of FCM. One-dimension cellular automata are used to code weight parameters, the cellular states are chosen within the range [0, 1] to form a cell space. In order to guide the optimization direction effectively and accelerate the speed of convergence, a mutation operator is added in the algorithm. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.
Cognitive map is a well-known approach to model the dynamics of qualitative systems, and has been studied and used in various fields, such as psychology, education, engineering, and management. Although the validity and usefulness of cognitive maps has been proven in many fields, and a considerable number of cognitive maps have been built during the last decade, cognitive map construction and use was just one-off event. In addition, the high degree of cognitive complexities in large cognitive maps makes it difficult for others to understand and exploit the pre-defined cognitive map in another similar domain problem.
In this paper, an ontological semantic inference method, which combines the cognitive map and semantic influence, is proposed. This approach reuses a pre-defined cognitive map and provides an ontological semantic inference mechanism in decision making environments by reducing the degree of cognitive complexities in a large cognitive map.
Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.
The theory of fuzzy power sets, which has hitherto been insufficiently developed, is shown very naturally to require the use of a fuzzy implication operator (Section 1). Six such operators are gathered from the literature on multiple-valued logic (Section 2), and their effects on fuzzy power-set theory are compared throughout the rest of the paper. After certain fundamental definitions of set characteristics (Section 3), the six operators are carried in parallel while working out basic aspects of power-set theory. Among these are the properties of the set-inclusion relation and the set-equivalence relation (Section 4), two distinct concepts of disjointness (Section 5), questions of consistency in the relations between a set and its complement (Section 6), and a very concrete theorem on a difference among the operators with regard to the derivation of crisp conclusions from fuzzy premises (Section 7). Finally (Section 8), emphasis is placed on the dependence of the choice of operators upon the purposes the user has in hand.
The authors report a novel interface to a spatial analysis system which allows the underlying geographical domain to be represented using a high-level, feature-or object-oriented model. The system incorporates an intelligent, data-driven reporting technique which analyses the domain characteristics in order to highlight significant trends. A second novel feature is the use of data held in our extended data model for the purpose of query optimization. The interface is based on object-oriented techniques, which have been enhanced to capture contextual data and knowledge, including general patterns of data behaviour, general geographical knowledge and database domain-specific characteristics. A geographical information system can offer more flexibility when it can handle searches in both a spatial and object-centred way. Our current work is concerned with increasing the functionality and efficiency of the object-centred approach, and hence increasing the effectiveness of geographical information systems as an aid to analysis and decision-making, especially where very large volumes of data are involved.
An abstract is not available.
Fuzzy cognitive maps (FCMs) are fuzzy-graph structures for representing causal reasoning. Their fuzziness allows hazy degrees of causality between hazy causal objects (concepts). Their graph structure allows systematic causal propagation, in particular forward and backward chaining, and it allows knowledge bases to be grown by connecting different FCMs. FCMs are especially applicable to soft knowledge domains and several example FCMs are given. Causality is represented as a fuzzy relation on causal concepts. A fuzzy causal algebra for governing causal propagation on FCMs is developed. FCM matrix representation and matrix operations are presented in the Appendix.
The authors present a method for coping with the uncertainty and
fuzziness that cause problems in handling information. As these two
concepts carry different semantic meanings, they distinguish between
them by representing fuzzy information as conjunctive fuzzy sets and
uncertainty by means of generalized fuzzy sets. An object-oriented
methodology is chosen because it offers an excellent framework for
handling uncertainty in a way completely transparent to the user. This
treatment of uncertainty in a fuzzy object-oriented environment is
well-suited to databases; hence examples from this field are selected.
Genealogical databases, where uncertain information is very common, and
where information is continuously updated, are used as a frame of
reference. The proposed model also allows storage of as much information
about uncertain objects as possible
This paper describes the development of the data model of PROBE, a knowledge-oriented DBMS being developed at CCA [DAYA85, DAYA86]. The data model, called PDM, is an extension of the Daplex functional data model [SHIP81, FOX84] that illustrates an integration of functional, relational, and object-oriented approaches. The extensions are primarily those required to handle the requirements of new database applications, such as engineering applications and cartography, having spatial or temporal semantics.
The object-oriented data model has been utilized for geographical information applications, chiefly because of the rich set of modelling primitives it offers. Despite its representational capabilities, it still falls short in describing data typically seen in geographic information systems, i. e., imprecise, spatial or continuous valued data. In this paper, an extension to the object-oriented data model that permits the representation of imprecise data is discussed in the context of a soil information system. It is shown that this extension accommodates the querying and manipulation of spatial and continous valued data.
Designing a system that is able to make use of quantitative and
qualitative data for real world applications is a challenging problem.
Traditional systems produce representational descriptions that are often
not very useful to the human expert. To rectify this problem we propose
a structure based on contextual fuzzy cognitive maps (CFCMs) for
geographic information systems (GISs). Our framework builds this
structure using both spatial and temporal information to gain
quantitative and qualitative descriptions. In addition, these cognitive
maps are able to provide generalized descriptions that reflect
relationships between landmarks. Such a scheme is capable of producing
cognitive descriptions similar to those a human expert might derive and
use. In the paper, we illustrate the types of CFCMs we can generate
using real census data, human expert knowledge, and quantitative data in
the form of maps in a GIS. For a given goal, our system structure is
hierarchical by context, multilayered by variations in data over periods
of time, and semi-qualitative in that the CFCMs build causal links and
relationships between landmarks and concepts
An object-oriented approach to the management of geographical data
- E Oxborrow
- Z Kemp
E. Oxborrow and Z. Kemp, " An object-oriented approach to the manage-ment of geographical data, " in Proc. Int. Conf on Managing Geographic Data and Databases, Lancaster, U.K., June 1989, pp. 96–108.
Object-oriented FCM's applied to geographic information systems
- R Satur
- L Zhi-Qiang
- M Gahegan
R. Satur, L. Zhi-Qiang, and M. Gahegan, " Object-oriented FCM's applied to geographic information systems, " in Proc. FUZZ-IEEE/IFES'95, Yokohama, Japan, Mar. 20–24, 1995, 1997.
Uncertainty modeling in object-oriented databases—A fuzzy logic based approach
- R George
- R Srikanth
- F E Perty
- B P Buckles
R. George, R. Srikanth, F. E. Perty, and B. P. Buckles, " Uncertainty modeling in object-oriented databases—A fuzzy logic based approach " in Proc. 19th Int. Conf. Very Large Data Bases, Dublin, Ireland, Aug. 1993.