Nonlinear predictive modeling using dynamic non-singleton fuzzy logic systems

Author

Mouzouris, George C. ; Mendel, Jerry M.

Author_Institution

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

Volume

2

fYear

1996

fDate

8-11 Sep 1996

Firstpage

1217

Abstract

We investigate the dynamic versions of fuzzy logic systems (FLSs), and specifically their nonsingleton 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. Since dynamic NSFLSs 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

adaptive systems; autoregressive moving average processes; fuzzy logic; fuzzy systems; generalisation (artificial intelligence); learning systems; modelling; nonlinear dynamical systems; NARMA models; adaptive systems; dynamic learning algorithm; dynamic nonsingleton fuzzy logic systems; nonlinear autoregressive moving average models; nonlinear predictive modeling; nonsingleton generalizations; Backpropagation algorithms; Fuzzy logic; Fuzzy systems; Image processing; Neural networks; Nonlinear dynamical systems; Output feedback; Predictive models; Signal processing; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on

Conference_Location

New Orleans, LA

Print_ISBN

0-7803-3645-3

Type

conf

DOI

10.1109/FUZZY.1996.552351

Filename

552351