Application of self-organizing network and MLP for fuzzy rule extraction

Author

Gaweda, Adam E. ; Zurada, Jacek M.

Author_Institution

Dept. of Electr. Eng., Louisville Univ., KY, USA

Volume

6

fYear

1999

fDate

36342

Firstpage

4289

Abstract

A multi-stage algorithm for classification and fuzzy rule extraction from data based on a self-organizing network and a two-layer perceptron network is proposed. Self-organizing techniques are applied to find data prototypes, which are subsequently used to initialize the perceptron network and to produce membership functions from the learned mapping. Fuzzy rules created by the algorithm provide linguistic description of the relationship encoded in data

Keywords

fuzzy neural nets; fuzzy set theory; inference mechanisms; knowledge acquisition; multilayer perceptrons; pattern classification; self-organising feature maps; unsupervised learning; fuzzy inference; fuzzy rule extraction; fuzzy set theory; knowledge acquisition; membership functions; multilayer perceptron; pattern classification; self-organizing network; unsupervised learning; Clustering algorithms; Data mining; Electronic mail; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Inference algorithms; Neural networks; Prototypes; Self-organizing networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.830856

Filename

830856