A COMPARATIVE STUDY BETWEEN A MULTI-MODELS BASED APPROACH AND AN ARTIFICIAL NEURAL NETWORK BASED TECHNIQUE FOR NONLINEAR SYSTEMS IDENTIFICATION

Authors

  • Lamine Thiaw
  • Mariusz Rybnik
  • Rachid Malti
  • Abdennasser Chebira
  • Kurosh Madani

DOI:

https://doi.org/10.47839/ijc.3.1.255

Keywords:

Multi-Model Structure, Neural Networks, Self-organizing Architecture, Multi-model generator, Identification, Nonlinear System, Comparative Study

Abstract

Recently, a number of works propose multi-model based approaches to model non-linear systems. Such approach could also been seen as some “specific” approach, inspired from ANN operation mode, where each neurone, represented by one of the local models, realizes some higher level transfer function. In this paper we present two different neural based approaches to such multiple models concept: one issued from conventional structure and the other based on self-organizing dynamic architecture. A comparative study between a multi-model based architecture and an ANN based one, in the frame of nonlinear system identification is reported.

References

N. Wiener, Non linear Problems in Random Theory, Technology Press MIT, and John Wiley, New York (1958).

M. Schetzen, The Voltera and Wiener Theories of Nonlinear Systems, John Wiley, New York, (1980)

L. Zadeh, Outline of a New Approach to the Analysis of Complex Systems and Decision Processes, IEEE Trans. On Systems, Man and Cybernetics 3, pp. 28-44

T. Takagi and M. Sugeno, Fuzzy identification of systems and its application to modeling and control. IEEE Trans. on Systems Man and Cyberneticc, Vol. 15, (1985). pp. 116-132

Boukhris, A, Mourot G. and Ragot J. (2000). Nonlinear dynamic system identification: a multiple-model approach. Int. J. of control, Vol. 72, N°7/8, pp. 591-604

K.S. Narendra, and K. Parthasarath., Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Networks, Vol. 1, No. 1, (1990).

O. Nelles, On the identification with neural networks as series-parallel and parallel models, Int. Conf. on Artificial Neural Networks (ICANN’95), Paris, France, (1995).

Multiple Model Approaches to Modeling and Control, edited by R. Murray-Smith and T.A. Johansen, Taylor & Francis Publishers, (1997), ISBN 0-7484-0595-X.

M. Mayoubi, M. Schafer, S. Sinsel, Dynamic Neural Units for Non-linear Dynamic Systems Identification, LNCS Vol. 930, Springer Verlag, (1995), pp.1045-1051.

S. Ernst, Hinging hyper-plane trees for approximation and identification, 37th IEEE Conf. on Decision and Control, Tampa, Florida, USA, (1998)

K. Madani, M. Rybnik, A. Chebira, Data Driven Multiple Neural Network Models Generator Based on a Tree-like Scheduler, LNCS series, Edited by: J. Mira, A. Prieto - Springer Verlag (2003), ISBN 3-540-40210-1, pp. 382-389

K. Madani, M. Rybnik, A. Chebira, Non Linear Process Identification Using a Neural Network Based Multiple Models Generator, LNCS series, Edited by: J. Mira, A. Prieto - Springer Verlag, (2003). ISBN 3-540-40211-X, pp. 647-654.

Downloads

Published

2014-08-01

How to Cite

Thiaw, L., Rybnik, M., Malti, R., Chebira, A., & Madani, K. (2014). A COMPARATIVE STUDY BETWEEN A MULTI-MODELS BASED APPROACH AND AN ARTIFICIAL NEURAL NETWORK BASED TECHNIQUE FOR NONLINEAR SYSTEMS IDENTIFICATION. International Journal of Computing, 3(1), 66-74. https://doi.org/10.47839/ijc.3.1.255

Issue

Section

Articles