文档介绍：International Journal putational Cognition (.htm)
Volume 2, Number 3, Pages 137–153, September 2004
Publisher Item Identifier S 1542-5908(04)10308-4/$
Article electronically published on June 27, 2003 at . Please cite this
paper as: hAmine TRABELSI, Frederic LAFONT, Mohamed KAMOUN, Gilles ENEA, “Identification
of Nonlinear Multivariable Systems by Adaptive Fuzzy Takagi-Sugeno Model”, International Journal
putational Cognition (.htm), Volume 2, Number 3, Pages 137–153,
September 2004i.
IDENTIFICATION OF NONLINEAR MULTIVARIABLE
SYSTEMS BY ADAPTIVE FUZZY TAKAGI-SUGENO
MODEL
AMINE TRABELSI, FREDERIC LAFONT, MOHAMED KAMOUN, GILLES ENEA
Abstract. This paper investigates the use of a fuzzy method as a
tool for model identification of a non linear and multivariable system
when the measurement data is available. In fact, the use of fuzzy
clustering facilitates automatic generation of Takagi-Sugeno rules and
its antecedent parameters. After the determination of the consequent
parameters, these are adapted by a recursive least squares algorithm
with a forgetting factor in order to use the established model in an
adaptive control scheme.
Copyright°c 2003 Yang’s Scientific Research Institute, LLC. All rights
reserved.
1. INTRODUCTION
For nonlinear dynamic systems, the conventional techniques of modeling
and identifications are difficult to implement and sometimes impracticable.
However, others techniques based on fuzzy logic are more and more used for
modeling this kind of process [4]. Among the different fuzzy methods, the
Takagi-Sugeno model (TS) has attracted most attention [18]. In fact, this
model consists of if-then rules with fuzzy antecedents and mathematical
functions in the consequent part. The task of system identification is to
determine both the non linear parameters of the antecedents and the linear
parameters of the rules consequent.
In general, there are two ways to obtain this information. Human experts
may be