Bayesian selective combination of multiple neural networks for improving long-range predictions in nonlinear process modelling

2004 ◽  
Vol 14 (1) ◽  
pp. 78-87 ◽  
Author(s):  
Zainal Ahmad ◽  
Jie Zhang
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Nonlinear Process ◽  
Process Modelling ◽  
Multiple Neural Networks

Improving Nonlinear Process Modelling Through Selective Combination of Multiple Neural Networks using Combined Correlation Coefficient Analysis

2012 ◽  
Author(s):  
Zainal Ahmad ◽  
Rabiatul ‘Adawiyah Mat Noor
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Correlation Coefficient ◽  
Network Models ◽  
Nonlinear Process ◽  
Process Modelling ◽  
Combination Method ◽  
Neural Network Models ◽  
Multiple Neural Networks ◽  
Highly Correlated

This paper proposed a selective combination method based on combined correlation coefficient analysis to increase the robustness of the single neural network. The main objective of the proposed approach is to improve the generalisation capability of the neural network models by combining networks that are less correlated. The assumption that we made is that combining networks that are highly correlated might not improve the final prediction performance due to the fact that these networks present the same contribution to the final prediction. This might even deteriorate the robustness of the combined network. The result shows that combination multiple neural networks using the proposed approach improved the performance of the two nonlinear process modelling case studies in which there is a significant reduction of validation sum square error (SSE) of the networks was obtained. Key words: Multiple neural networks, selective combination neural networks, correlation coefficient, nonlinear process modelling

scholarly journals OPTIMUM NUMBERS OF SINGLE NETWORK FOR COMBINATION IN MULTIPLE NEURAL NETWORKS MODELING APPROACH FOR MODELING NONLINEAR SYSTEM

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Zainal Ahmad ◽  
Rabiatul Adawiah Mat Noor
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Process ◽  
Process Models ◽  
Optimum Number ◽  
Modeling And Control ◽  
Proposed Model ◽  
Multiple Neural Networks ◽  
Combination Approach ◽  
Multiple Network

This paper is focused on finding the optimum number of single networks in multiple neural networks combination to improve neural network model robustness for nonlinear process modeling and control. In order to improve the generalization capability of single neural network based models, combining multiple neural networks is proposed in this paper. By studying the optimum number of network that can be combined in multiple network combination, the researcher can estimate the complexity of the proposed model then obtained the exact number of networks for combination. Simple averaging combination approach is implemented in this paper which is applied to nonlinear process models. It is shown that the optimum number of networks for combination can be obtained hence enhancing the performance of the proposed model.

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