Dynamic non-Singleton fuzzy logic systems for nonlinear modeling

Author

Mouzouris, George C. ; Mendel, Jerry M.

Author_Institution

Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA

Volume

5

Issue

2

fYear

1997

fDate

5/1/1997 12:00:00 AM

Firstpage

199

Lastpage

208

Abstract

We investigate dynamic versions of fuzzy logic systems (FLSs) and, specifically, their non-Singleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonlinear dynamic system identification. Since dynamic NSFLS´s can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLSs in the predictive modeling of a NARMA process

Keywords

autoregressive moving average processes; fuzzy logic; fuzzy systems; identification; learning (artificial intelligence); modelling; nonlinear dynamical systems; NARMA models; dynamic learning; identification; non-Singleton fuzzy logic systems; nonlinear autoregressive moving average; nonlinear dynamic systems; nonlinear modeling; Artificial neural networks; Autoregressive processes; Backpropagation algorithms; Bridges; Fuzzy logic; Heuristic algorithms; Nonlinear dynamical systems; Power system modeling; Predictive models; System identification;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.580795

Filename

580795