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A multimodel approach for a nonlinear system based on neural network validity

A multimodel approach for a nonlinear system based on neural network validity Purpose – The purpose of this paper is to present a new concept based on a neural network validity approach in the area of multimodel for complex systems. Design/methodology/approach – The multimodel approach was recently developed in order to solve the modeling problems and the control of complex systems. The strategy of this approach coincides with the usual approach of the engineer which consists in subdividing a complex problem to a set of simple, manageable sub‐problems that can be solved separately. However, this approach still faces some problems in design, especially in determining models and in finding the appropriate method of calculating validities. Findings – A novel approach based on neural network validity shows very remarkable performances in multimodel for complex systems. Research limitations/implications – The validity of each model is based on the convergence of each neural network. For a fast convergence the proposed approach can be online to give a good performance in multimodel representation for system with rapid dynamics. Practical implications – The proposed concept discussed in the paper has the potential to be applied to complex systems. Originality/value – The suggested approach is implemented and reviewed with a complex dynamic and fast process compared to the residue approach commonly used in the calculation of validities. The results prove to be satisfactory and show a good accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

A multimodel approach for a nonlinear system based on neural network validity

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781111160011
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to present a new concept based on a neural network validity approach in the area of multimodel for complex systems. Design/methodology/approach – The multimodel approach was recently developed in order to solve the modeling problems and the control of complex systems. The strategy of this approach coincides with the usual approach of the engineer which consists in subdividing a complex problem to a set of simple, manageable sub‐problems that can be solved separately. However, this approach still faces some problems in design, especially in determining models and in finding the appropriate method of calculating validities. Findings – A novel approach based on neural network validity shows very remarkable performances in multimodel for complex systems. Research limitations/implications – The validity of each model is based on the convergence of each neural network. For a fast convergence the proposed approach can be online to give a good performance in multimodel representation for system with rapid dynamics. Practical implications – The proposed concept discussed in the paper has the potential to be applied to complex systems. Originality/value – The suggested approach is implemented and reviewed with a complex dynamic and fast process compared to the residue approach commonly used in the calculation of validities. The results prove to be satisfactory and show a good accuracy.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Aug 23, 2011

Keywords: Nonlinear system; Multimodel; Neural network validity; Complex systems; Modelling; Non‐linear control systems

References