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An unsupervised neural network using a fuzzy learning rule

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1 Author(s)
Yong Soo Kim ; Dept. of Comput. Eng., Taejon Univ., South Korea

This paper presents a fuzzy neural network which utilizes a similarity measure of the relative distance and a fuzzy learning rule. A fuzzy learning rule consists of a fuzzy membership value, an intra-cluster membership value, and a function of the number of iterations. The proposed fuzzy neural network updates weights of all committed output neurons regardless of winning or losing. The proposed fuzzy neural network is evaluated using the IRIS data set.

Published in:

Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International  (Volume:1 )

Date of Conference:

22-25 Aug. 1999