A comparative study of learning methods in tuning parameters of fuzzy membership functions

Author

Chen, Mu-Song

Author_Institution

Dept. of Electr. Eng., Da-Yeh Univ., Chang-Hwa, Taiwan

Volume

3

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

40

Abstract

We compare several popular training algorithms for tuning parameters of fuzzy membership functions (MFs). The algorithms compared are gradient descent (GD), resilient propagation (RPROP), Quickprop (QP), and Levenberg-Marquardt (LM) algorithms. These algorithms are combined with RLSE (recursive least squares estimate) to improve the efficiency of an ANFIS (adaptive network-based fuzzy inference system). The results, on average, show that the relative performance of these algorithms depends on the given task, but that RPROP produces better performance in terms of convergence speed, stability, and generalization properties

Keywords

convergence; fuzzy logic; fuzzy set theory; inference mechanisms; learning (artificial intelligence); least squares approximations; neural nets; parameter estimation; tuning; ANFIS; Levenberg-Marquardt algorithms; Quickprop; adaptive network-based fuzzy inference system; fuzzy membership functions; gradient descent; learning methods; recursive least squares estimate; resilient propagation; Adaptive systems; Convergence; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Learning systems; Least squares approximation; Quadratic programming; Recursive estimation; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.823150

Filename

823150