Using fuzzy partitions to create fuzzy systems from input-output data and set the initial weights in a fuzzy neural network

Author

Lin, Yinghua ; Cunningham, George A., III ; Coggeshall, Stephen V.

Author_Institution

Center for Nonlinear Studies, Los Alamos Nat. Lab., NM, USA

Volume

5

Issue

4

fYear

1997

fDate

11/1/1997 12:00:00 AM

Firstpage

614

Lastpage

621

Abstract

We create a set of fuzzy rules to model a system from input-output data by dividing the input space into a set of subspaces using fuzzy partitions. We create a fuzzy rule for each subspace as the input space is being divided. These rules are combined to produce a fuzzy rule based model from the input-output data. If more accuracy is required, we use the fuzzy rule-based model to determine the structure and set the initial weights in a fuzzy neural network. This network typically trains in a few hundred iterations. Our method is simple, easy, and reliable and it has worked well when modeling large “real world” systems

Keywords

fuzzy control; fuzzy neural nets; fuzzy systems; binary fuzzy partitions; fuzzy neural network; fuzzy rules; fuzzy systems; input-output data; large real world systems; Decision trees; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Intelligent networks; Knowledge based systems; Laboratories; Neural networks; Nonlinear systems;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.649913

Filename

649913