Neuro-fuzzy network based on rough sets and its applications

Author

Zhang, Jianming ; Wang, Shuqing ; Xie, Lei

Author_Institution

Res. Inst. of Adv. Process Control, Zhejiang Univ., Hangzhou, China

Volume

3

fYear

2003

fDate

5-8 Oct. 2003

Firstpage

2803

Abstract

A new constructive method of the neuro-fuzzy network based on rough sets is proposed. First, an initial fuzzy rule base is generated from the history input-output data pairs by rough sets approach. Then, a neuro-fuzzy network is formed according to the rule table. And the learning algorithm based on the gradient descent method is given. The major advantage of this approach is to optimize the overall structure of the neuro-fuzzy network as well as to adjust each parameter of fuzzy rules without doing the complicated clustering process. Finally, the efficiency of the new method is illustrated by means of applying to truck backer-upper control.

Keywords

fuzzy neural nets; fuzzy set theory; fuzzy systems; gradient methods; knowledge based systems; learning (artificial intelligence); optimisation; rough set theory; clustering process; fuzzy rules; gradient descent method; learning algorithm; neuro fuzzy network; optimization; rough sets; truck backer upper control; Clustering algorithms; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Industrial control; Laboratories; Neural networks; Process control; Rough sets; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2003. IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-7952-7

Type

conf

DOI

10.1109/ICSMC.2003.1244310

Filename

1244310