A wavelet-based neuro-fuzzy system and its applications

Author

Lin, Cheng-Jian ; Chin, Cheng-Chung ; Lee, Cheng-Ling

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Chaoyang Univ. of Technol., Taichung, Taiwan

Volume

3

fYear

2003

fDate

20-24 July 2003

Firstpage

1921

Abstract

This paper addresses a wavelet-based neuro-fuzzy system (WNFS) for non-linear system identification and control. The WNFS combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural network (WNN). Each fuzzy rule corresponding to a WNN consists of single-scaling wavelets. We adopt the non-orthogonal and compactly supported functions as wavelet neural network bases. The on-line structure/parameter learning algorithm is performed concurrently in the WNFS. The several simulation examples have been given to illustrate the performance and effectiveness of the proposed model.

Keywords

fuzzy neural nets; fuzzy set theory; identification; learning (artificial intelligence); nonlinear systems; wavelet transforms; Takagi-Sugeno-Kang fuzzy model; fuzzy rule; nonlinear system control; nonlinear system identification; nonorthogonal functions; online parameter learning algorithm; single scaling wavelets; supported functions; wavelet based neural fuzzy system; wavelet neural network; Application software; Chaos; Computer science; Computer science education; Feedforward neural networks; Function approximation; Fuzzy neural networks; Neural networks; Shape; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223701

Filename

1223701