A new approach to fuzzy-neural system modeling

Author

Lin, Yinghua ; Cunningham, George A., III

Author_Institution

Dept. of Comput. Sci., New Mexico Inst. of Min. & Technol., Socorro, NM, USA

Volume

3

Issue

2

fYear

1995

fDate

5/1/1995 12:00:00 AM

Firstpage

190

Lastpage

198

Abstract

We develop simple but effective fuzzy-rule based models of complex systems from input-output data. We introduce a simple fuzzy-neural network for modeling systems, and we prove that it can represent any continuous function over a compact set. We introduce “fuzzy curves” and use them to: 1) identify significant input variables, 2) determine model structure, and 3) set the initial weights in the fuzzy-neural network model. Our method for input identification is computationally simple and, since we determine the proper network structure and initial weights in advance, we can train the network rapidly. Viewing the network as a fuzzy model gives insight into the real system, and it provides a method to simplify the neural network

Keywords

fuzzy neural nets; identification; large-scale systems; modelling; neural net architecture; complex systems; fuzzy curves; fuzzy-neural network; fuzzy-neural system modeling; fuzzy-rule based models; initial weights; input identification; model structure; Computer architecture; Computer networks; Data mining; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Input variables; Modeling; Neural networks;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.388173

Filename

388173