Non Linear Process Identification Using a Neural Network Based Multiple Models Generator

Authors:
Kurosh Madani;Mariusz Rybnik;Abdennasser Chebira
Affiliations:
Intelligence in Instrumentation and Systems Laboratory (I2S), Senart Institute of Technology, University PARIS XII, Lieusaint, France F-77127;Intelligence in Instrumentation and Systems Laboratory (I2S), Senart Institute of Technology, University PARIS XII, Lieusaint, France F-77127;Intelligence in Instrumentation and Systems Laboratory (I2S), Senart Institute of Technology, University PARIS XII, Lieusaint, France F-77127
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

Identification of non-linear systems is an important task for model based control, system design, simulation, prediction and fault diagnosis. In real world applications, strong linearity and large number of related parameters make the realization of those steps challenging, and so, the identification task difficult. Recently, a number of works based on Multiple Modelling have been proposed to avoid difficulties related to non-linearity. In this paper we use an Artificial Neural Network based data driven Multiple Model generator, that we called T-DTS (Treelike Divide To Simplify), for non-linear systems identification. T-DTS reduces modeling complexity on both data and processing levels. The efficiency of such approach has been analyzed trough two applications dealing with none-linear process identification. Experimental results validating our approach have been reported.