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Pattern recognition is increasingly becoming a key component of decision support systems (DSSs) in many application areas, especially when automatically extracting semantic rules from data is a chief concern. Accordingly, this paper presents a novel evolving neuro-fuzzy DSS, the generic self-organizing fuzzy neural network realizing Yager inference...
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Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relati...
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... GU and Zhang [12] combine the vector isolation and adaptive resonance theory as clustering algorithm of FNN structure identification. Oentaryo and Pasquier [13] propose a new FNN system-Generic Self-Organizing GSOFNN which applies discrete enhanced clustering algorithm to learn fuzzy neural network structure. The rule base of GSOFNN is constant and compact because it has a set of mechanisms to delete redundant rules. ...
Traditional fuzzy neural network tends to be used for parameters identification and the network structure is separated by grids, so there are obvious defects in this mode of control design. This paper introduces a flexibly model of self-organizing fuzzy neural network according to specific network structure. We analyze its structure and containing parameters and propose an improved nearest neighbor clustering algorithm first for the predicting model of online identification. For parameter optimization, the parameter value acquired at self-organizing learning phase is adopted as the initial value of supervised learning. Then it adopts BP algorithm to adjust the parameter to optimal value based on the same training set, so as to acquire the final model of FNN. The experiments demonstrate that our algorithm can solve the predicting problems of nonlinear system with constraints, and the range and changing rate of control signal. It shows rapid computing speed, better stability and strong anti-disturbance capacity. It is also verified to be suitable for actual engineering control environments.
... In the former, learning and inference are accomplished via a global activation of the entire rule base (i.e., the underlying network). Globalized NFSs, such as (Angelov & Zhou, 2008;Chakraborty & Pal, 2004;Jang, 1993;Kasabov, 2001;Lin & Lin, 1997;Liu, Quek, & Ng, 2007;Oentaryo, Pasquier, & Quek, 2008), typically exhibit good accuracy and generalization performances, since all network parameters are utilized to compute the output for any given input. However, the acquisition of new information in these systems affects all parameters and may cause a catastrophic interference with (or forgetting of) knowledge previously gained. ...
... NFS is a powerful, hybrid symbolicconnectionist model that exploits a neural network's learning ability, parallelism, and robustness to induce high-level fuzzy linguistic rules from low-level data. 7,8 It facilitates humanlike approximate reasoning by handling uncertainty and imprecision in numerical data while allowing highlevel processing at the semantic and symbolic levels. ...
... 9 A plausible implementation of this module is via the globalist NFS. 7 Lastly, the executive manager is used for active monitoring, control, and orchestration of cognitive processes to enhance overall system perfor mance. This role maps functionally to the frontal cortex, 9 such as to change or interrupt ongoing processes in the transient, procedural, and declarative memories; tune external (free) parameters; and so on. ...
Recent advances in cognitive neuroscience, psychology, and AI provide remarkable insights toward building an integrated framework for human-level, general intelligence. This article presents Integrated Neuro-Cognitive Architecture (INCA), which emulates the putative functional aspects of various major brain systems via a learning memory modeling approach. INCA features scalable structural and parameter self-organizing mechanisms to form high-level symbolic knowledge from low-level data and knowledge exploitation mechanisms based on plausible consolidation and inference cycles, respectively.
... Similar to GenS-oFNN, eFSM may also take on other forms of fuzzy reasoning schema such as truth-value restriction (Mantaras, 1990), approximate analogical reasoning (Turksen & Zhong, 1990) and yager reasoning (Yager, Keller, & Tahani, 1992). The reader may refer to Tung and Quek (2005), Tung and Quek (2006) and Oentaryo, Pasquier and Quek (2008) for more details. ...
Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.Research highlights► A straddle trading framework based on eFSM neural-fuzzy model and MACD principle. ► eFSM dynamically models and forecasts the volatility trends of the Hang Seng Index. ► MACD generates timely trading signals using projected volatility trends from eFSM. ► Straddles are bought and sold based on the MACD signals. ► Trading framework achieves significantly higher return than time deposit account.
... With the neural implementation of a fuzzy system, the derivation of the fuzzy rules (structural learning) and the automatic tuning of the parameters characterizing the embedded fuzzy model (parameter learning) can now be performed using the learning and optimization methods extensively studied in the neural network domain. This subsequently leads to the development of many NFSs that include the adaptive-network-based fuzzy inference system (ANFIS) [6], the pseudo outer-product fuzzy neural network (POPFNN) and its family of networks [7]- [11], the fuzzy adaptive learning control network (FALCON) and its variants [12]- [14], the hybrid neural fuzzy inference system (HyFIS) [15], the evolving connectionist system (ECoS)-based networks [16], the evolving fuzzy neural network (EFuNN) [16], [17], the dynamic evolving neurofuzzy inference system (DENFIS) [18], the generic self-organizing fuzzy neural network (GenSoFNN) and its family of networks [19]- [23], and the fuzzy cerebellar model articulation controller (CMAC)-based networks [24]- [28]. A comprehensive review of the rule generation techniques employed for the structural identification of an NFS is reported in [29]. ...
... The primary reason why conventional gradient-descent learning is not suitable for the tuning of the fuzzy set parameters in a Mamdani-type rule-base system such as the proposed eFSM is that it focuses only on improving the global performance (i.e., output accuracy) of the embedded fuzzy model. This can be easily deduced from an analysis of the gradient-descent learning equations derived in [59], which dwells on the unilateral optimization of in (22) or with respect to each output of a multioutput system, centers on minimizing the specific output error of (23). Such devotion to global accuracy runs contrary to the more important need of having good interpretability of the associated fuzzy rule bases when employing Mamdani-type fuzzy rule-base systems for linguistic fuzzy modeling. ...
Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. T-
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his enables eFSM to maintain a current and compact set of Mamdani-type if-then fuzzy rules that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled. The learning and modeling performances of the proposed eFSM are evaluated using several benchmark applications and the results are encouraging.
... where F k is the uncertain firing strength of the rule R k due to the noise corrupted inputs, as defined in (11). The fuzzy Yager inference scheme adopts the disjunctive model of fuzzy relation, where conclusions from multiple, parallel rules have to be combined in a conjunctive manner [11]. ...
... where F k is the uncertain firing strength of the rule R k due to the noise corrupted inputs, as defined in (11). The fuzzy Yager inference scheme adopts the disjunctive model of fuzzy relation, where conclusions from multiple, parallel rules have to be combined in a conjunctive manner [11]. SinceÕL lm,m may serve as output to more than one fuzzy rule, the overall inferred fuzzy output setỸ lm,m from OL lm,m due to the activation of the rulebase of the proposed T2-HyFIS-Yager network is given as in (15) ...
The hybrid neural fuzzy inference system (Hy-FIS) is a five layers adaptive neural fuzzy inference system, based on the compositional rule of inference (CRI) scheme, for building and optimizing fuzzy models. To provide the HyFIS architecture with a firmer and more intuitive logical framework that emulates the human reasoning and decision-making mechanism, the fuzzy Yager inference scheme, together with the self-organizing Gaussian discrete incremental clustering (gDIC) technique, were integrated into the HyFIS network to produce the HyFIS-Yager-gDIC . This paper presents T2-HyFIS-Yager, a type-2 hybrid neural fuzzy inference system realizing Yager inference, for learning and reasoning with noise corrupted data. The proposed T2-HyFIS-Yager is used to perform time-series forecasting where a non-stationary time-series is corrupted by additive white noise of known and unknown SNR to demonstrate its superiority as an effective neuro-fuzzy modeling technique.
... This model was built upon the localized learning trait of the cortical circuits in the brain, and expanded to model fuzzy linguistic rules to provide system interpretability. Meanwhile, the FLM is realized using a globalist NFS model [16], whereby learning and inference are accomplished via global activation of the entire knowledge base. The model emulates the hippocampal role in rapidly encoding perceptual information contributing to our experiences or episodic knowledge, and thus exhibits a pattern separation trait i.e., distinct events are encoded using separate, non-overlapping (rule) representations. ...
... Further details of the localized inference process to derive y s m can be found in [4]. Meanwhile, to get y f m , a modified version of the globalized inference method in [16] is used. This is given in (7), where m l,m is the centroid of consequent label C l,m , Z k l,m is the firing strength of a rule R k linked to C l,m , and L m is the number of labels in the m th output. ...
Fuzzy and neuro-fuzzy systems are increasingly among the key technologies employed in many real-world applications. However, traditional neuro-fuzzy systems are generally still lacking the scalability traits required in the face of large-scale data and the capability to incorporate new information without catastrophically disrupting the existing knowledge base. This work aims at addressing these issues by proposing a novel neuro-fuzzy system termed dual consolidation network (DCN) that models the complementary interactions between hippocampus and neocortex regions in the human brain to consolidate and exploit knowledge effectively. This approach allows the DCN to handle data sets with high-dimensional features and/or a very large number of samples efficiently, as well as to minimize interference when acquiring new information. Preliminary experiments employing DCN on large-scale biomedical data have shown encouraging results.
Diabetes Mellitus, generally called as High Sugar Problem is a metabolic disorder described by chronic hyperglycemia enhancing rapidly. It is a polygenic disease characterized by abnormal high glucose in the blood. Statistics say that 90 to 95 % of the World Diabetics have Type 2 Diabetes. And India tops the list above all countries. The consequences of diabetes may be macro vascular complication or micro vascular complication. This paper proposes a new model for prediction of complications developing due to Diabetes Mellitus. Data collection is done after discussion with Diabetologists of Diabetes care and Research centre. A questionnaire is prepared with seven stages after getting concern from Expert Doctor's opinion and Artificial Neural Network technique is used to predict the complications developing. As many researchers have contributed in diagnosis of Type 2 Diabetes, our work focuses on modeling an effective Diagnosis of a special complication called neuropathy.
Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.
Localist networks and especially neuro-fuzzy systems constitute promising techniques for data comprehension, but generally exhibit poor system interpretability and generalization ability. This paper aims at addressing the issues through a novel localist reduced fuzzy cerebellar model articulation controller (RFCMAC), that models the two-stage development of cortical memories in the human brain to compress and refine the formulated (fuzzy) rule base respectively. The proposed mechanisms allow the RFCMAC associative memory to induce a concise, interpretable rule base, and at the same time to improve generalization, fostering in turn system scalability and robustness. Experimental results on several benchmark tasks have demonstrated the potential of the proposed system as an effective tool for understanding data.
