An Experimental Validation of a New Method for Multimodel Identification-Reference-Cited by-同舟云学术

An Experimental Validation of a New Method for Multimodel Identification

Published:2017-10-06 Issue:2 Volume:140 Page:
ISSN:0022-0434
Container-title:Journal of Dynamic Systems, Measurement, and Control
language:en
Short-container-title:

Author:

Messaoud Anis¹,Abdennour Ridha Ben²

Affiliation:

1. Department of Electrical Engineering, National School of Engineers of Gabes, University of Gabes Tunisia, Gabes 6029, Tunisia e-mail:

2. Professor Department of Electrical Engineering, National School of Engineers of Gabes, University of Gabes Tunisia, Gabes 6029, Tunisia e-mail:

Abstract

In this paper, we propose a new method for an optimal systematic determination of models' base for multimodel identification. This method is based on the neural classification of data set picked out on a considered nonlinear system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models' base. A simulation example and an experimental validation on a chemical reactor are presented to evaluate the effectiveness of the proposed method.

Publisher

ASME International

Subject

Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/doi/10.1115/1.4037530/6126181/ds_140_02_024502.pdf

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