A new method for generating fuzzy classification systems using RBF neurons with extended RCE learning

Author

Halgamuge, S.K. ; Poechmueller, W. ; Pfeffermann, A. ; Schweikert, P. ; Glesner, M.

Author_Institution

Inst. of Microelectron. Syst., Darmstadt Univ. of Technol, Germany

Volume

3

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

1589

Abstract

A new method is presented combining the advantages of fuzzy inference and neural network learning. A three-layer radial basis function (RBF) network is used to extract rules and to identify the necessary membership functions of the inputs for a fuzzy classification system. The results obtained applying this new method to IRIS-classification are similar to that of other fuzzy-neural approaches, but only lesser number of rules and membership functions are necessary. This system based on RBF-neurons and extended restricted coulomb energy (RCE) learning allows very fast construction of expert knowledge only from input/output data without externally provided expert help and superfluous input features can be removed automatically after training the network

Keywords

feedforward neural nets; fuzzy neural nets; fuzzy systems; inference mechanisms; learning (artificial intelligence); RBF neurons; fuzzy classification systems; fuzzy inference; membership functions; radial basis function network; restricted coulomb energy learning; Euclidean distance; Feeds; Fuzzy neural networks; Fuzzy systems; Heuristic algorithms; Iris; Microelectronics; Neural networks; Neurons; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374393

Filename

374393