Neuro-fuzzy modeling of nonlinear systems for control purposes

Author

Culliere, Th ; Titli, A. ; Corrieu, J.M.

Author_Institution

Lab. d´´Autom. et d´´Anal. des Syst., CNRS, Toulouse, France

Volume

4

fYear

1995

fDate

20-24 Mar 1995

Firstpage

2009

Abstract

In this paper we propose a neuro-fuzzy model for the identification of the nonlinear dynamic systems. The new model is composed of two stages. The first stage consists of a cut-out of the input space in areas. This global treatment is done by the fuzzy module. The second stage consists of a local identification of the system by several simplified neural networks. This article describes the first stage with an independent simple fuzzy model and the second stage with a neural one. Then it presents the complete model and shows the modifications of the backpropagation algorithm for the multiple neural network´s learning. Simulations on examples and in particular on invert pendulum showing the neuro-fuzzy´s ability

Keywords

backpropagation; fuzzy neural nets; identification; nonlinear control systems; nonlinear dynamical systems; backpropagation algorithm modifications; input space cut-out; inverted pendulum; local identification; neuro-fuzzy modeling; nonlinear dynamic systems identification; Backpropagation algorithms; Control system synthesis; Fuzzy neural networks; Linear systems; Linearity; Mathematical model; Neural networks; Nonlinear control systems; Nonlinear systems; Power system modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int

Conference_Location

Yokohama

Print_ISBN

0-7803-2461-7

Type

conf

DOI

10.1109/FUZZY.1995.409954

Filename

409954