A fuzzy neural hybrid system modeling

Author

Türksen, I. Burhan

Author_Institution

Dept. of Mech.-Ind. Eng., Toronto Univ., Ont., Canada

Volume

4

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2337

Abstract

In this paper, we propose and discuss a fuzzy-neural system development schema. For this purpose, we identify three knowledge representation and approximate reasoning approaches. For the Type I fuzzy theory, we describe the extraction of fuzzy sets and fuzzy rules with the application of an improved fuzzy clustering technique which is essentially an unsupervised learning of the fuzzy sets and rules from a given input-output data set. Next we describe how this set of rules and their fuzzy sets may be adapted and/or modified for known target sets with supervised learning within a fuzzified neural network architecture. Finally, we introduce a unified (fuzzy) approximate reasoning formulation for fuzzy modeling and control

Keywords

expert systems; fuzzy control; fuzzy neural nets; fuzzy set theory; fuzzy systems; inference mechanisms; knowledge representation; learning (artificial intelligence); modelling; uncertainty handling; approximate reasoning; fuzzy clustering; fuzzy control; fuzzy expert systems; fuzzy neural network; fuzzy rules; fuzzy set theory; fuzzy-neural system; knowledge representation; modeling; supervised learning; unsupervised learning; Data mining; Fuzzy control; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Knowledge representation; Modeling; Supervised learning; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614428

Filename

614428