Title :
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
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;
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
DOI :
10.1109/FUZZY.1995.409954
