Powerful and flexible fuzzy algorithm for nonlinear dynamic system identification

Author

Schiavo, A. Lo ; Luciano, A.M.

Author_Institution

Dipt. di Ingegneria dell´´Informazione, Seconda Universita degli Studi di Napoli, Aversa, Italy

Volume

9

Issue

6

fYear

2001

fDate

12/1/2001 12:00:00 AM

Firstpage

828

Lastpage

835

Abstract

A new powerful and flexible fuzzy algorithm for nonlinear dynamic system identification is presented. It is based on the identification of the derivative of the system state, instead of the future system state. The membership functions of the underlying static fuzzy model are two-sided Gaussian functions and the learning algorithm is a hybrid-nested routine based on least-squares, quasi-Newton and simplex optimization methods. Moreover, a simple clustering algorithm based on an additional higher level fuzzy model is proposed. The application to the identification of the Mackey-Glass chaotic time series is presented and compared with previous results in terms of maximum error and nondimensional error index. Finally, the application to a test nonlinear dynamic system is presented to show the capabilities of the clustering algorithm. The obtained results show that the proposed algorithm can find wide application in practical problems, such as in nonlinear electronic circuit design

Keywords

fuzzy logic; fuzzy set theory; identification; mathematics computing; nonlinear dynamical systems; time series; Mackey-Glass chaotic time series; clustering algorithm; flexible fuzzy algorithm; fuzzy logic; higher level fuzzy model; hybrid-nested routine; learning algorithm; least-squares; maximum error; membership functions; nondimensional error index; nonlinear dynamic system identification; nonlinear electronic circuit design; nonlinear modeling; quasi-Newton; simple clustering algorithm; simplex optimization methods; static fuzzy model; system state derivative; test nonlinear dynamic system; two-sided Gaussian functions; Chaos; Circuit testing; Clustering algorithms; Fuzzy systems; Heuristic algorithms; Nonlinear dynamical systems; Optimization methods; Power system modeling; System identification; System testing;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.971732

Filename

971732