Constructing fuzzy model by self-organizing counterpropagation network

Author

Nie, Junhong

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

25

Issue

6

fYear

1995

fDate

6/1/1995 12:00:00 AM

Firstpage

963

Lastpage

970

Abstract

This paper describes a general and systematic approach to constructing a multivariable fuzzy model from numerical data through a self-organizing counterpropagation network (SOCPN). Two self-organizing algorithms USOCPN and SSOCPN, being unsupervised and supervised respectively, are introduced. SOCPN can be employed in two ways. In the first place, it can be used as a knowledge extractor by which a set of rules are generated from the available numerical data set. The generated rule-base is then utilized by a fuzzy reasoning model. The second use of the SOCPN is as an online adaptive fuzzy model in which the rule-base in terms of connection weights is updated successively in response to the incoming measured data. The comparative results on three well studied examples suggest that the method has merits of simple structure, fast learning speed, and good modeling accuracy

Keywords

backpropagation; fuzzy neural nets; knowledge acquisition; self-organising feature maps; backpropagation; fast learning speed; fuzzy model construction; fuzzy reasoning model; knowledge extractor; modeling accuracy; multivariable fuzzy model; numerical data set; online adaptive fuzzy model; rule-base; self-organizing counterpropagation network; simple structure; supervised self-organization; unsupervised self-organization; Data mining; Equations; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Helium; Humans; Mathematical model; Numerical models; Power system modeling;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.384258

Filename

384258