Figure 4 - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
Source publication
The GSTAR model is one linear space time model used to model time series data with inter-location linkages. One of the weaknesses of the GSTAR model is that the model has not been able to capture any nonlinear patterns that may arise. The ANN is one model of nonlinear artificial intelligence that has a flexible functional form and is a supervised e...
Contexts in source publication
Context 1
... GSTAR-ANN model is generally written as with is the input and is the weight that connects the input layer to the hidden layer with , and are the weights connecting from the hidden layer to the output layer and dan are the activation functions (in this case a the sigmoid function is used). Furthermore, this architecture is shown in Figure 4. Figure 4 shows the neuron p on the input layer, q neuron in the hidden layer, and 1 neuron at the output layer with the total parameters to be trained as bias and bias or or . ...
Context 2
... GSTAR-ANN model is generally written as with is the input and is the weight that connects the input layer to the hidden layer with , and are the weights connecting from the hidden layer to the output layer and dan are the activation functions (in this case a the sigmoid function is used). Furthermore, this architecture is shown in Figure 4. Figure 4 shows the neuron p on the input layer, q neuron in the hidden layer, and 1 neuron at the output layer with the total parameters to be trained as bias and bias or or . ...
Similar publications
In deep neural networks, the generalization is a vital evaluation metric. As it contributes to avoid over-fitting, Dropout plays an important role in improving the generalization of deep neural networks. Without fully utilizing the training data and the real-time performance of the networks, traditional Dropout and its variants lack of specificity...
Citations
... Some studies related to spatiotemporal time series modelling based on Zhang's univariate hybrid methodology [27] tried to delineate both linear and nonlinear data relationships [28][29][30][31][32][33]. For instance, [34] developed space time neural network to model the nonlinear spatiotemporal time series data, while [35] combined the generalized space time autoregressive (GSTAR) model with ANN to model the space time series. ...
A robust forecast of rice yields is of great importance for medium-to-long-term planning and decision-making in cereal production, from regional to national level. Incorporation of spatially correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space Time Autoregressive Moving Average (STARMA) is the most popular class of model in linear spatiotemporal time series modelling. However, STARMA cannot process nonlinear spatiotemporal relationships in datasets. Alternately, Time Delay Neural Network (TDNN) is a most popular machine learning algorithm to model the nonlinear pattern in data. To overcome these limitations, two-stage STARMA approach was developed to predict rice yield in some of the most intensive national rice agroecosystems in India. The Mean Absolute Percentage Errors value of proposed STARMA-II approach is lower compared to Autoregressive Moving Average (ARIMA) and STARMA model in all examined districts, while the Diebold-Mariano test confirmed that STARMA-II model is significantly different from classical approaches. The proposed STARMA-II approach is promising alternative to classical linear and nonlinear spatiotemporal time series models for estimating mixed linear and nonlinear patterns and can be advanced tool for mid-to-long-term sustainable planning and management of crop yields and patterns in agroecosystems, i.e., food supply and demand from local to regional levels.
Purpose
Because of the mechanical properties of aluminium (Al), an accurate prediction of its properties has been challenging. Researchers are seeking reliable models for predicting the mechanical strength of Al alloys owing to the continuous emergence of new Al alloys and their applications. There has been widespread use of empirical and statistical models for the prediction of different mechanical properties of Al and Al alloy, such as linear and nonlinear regression. Nevertheless, the development of these models requires laborious experimental work, and they may not produce accurate results depending on the relationship between the Al properties, mix of other compositions and curing conditions.
Design/methodology/approach
Numerous machine learning (ML) models have been proposed as alternative approaches for predicting the strengths of Al and its alloys. The hardness of Al alloys has been predicted by implementing various ML algorithms, such as linear regression, ridge regression, lasso regression and artificial neural network (ANN). This investigation critically analysed and discussed the application and performance of models generated by linear regression, ridge regression, lasso regression and ANN algorithms using different mechanical properties as training parameters.
Findings
Considering the definition of the problem, linear regression has been found to be the most suitable algorithm in predicting the hardness values of AA7XXX alloys as the model generated by it best fits the data set.
Originality/value
The work presented in this paper is original and not submitted anywhere else.
