Improvement of Noise Tolerance in Fuzzy ART Using a Weighted Sum and a Fuzzy AND Operation

Chang Joo LEE  Sang Yun LEE  Choong Woong LEE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E78-A   No.10   pp.1432-1434
Publication Date: 1995/10/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Artificial Intelligence and Knowledge
Fuzzy ART,  category proliferation,  weight vector,  slow learning,  fast-commit slow-recode,  

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This paper presents a new learning method to improve noise tolerance in Fuzzy ART. The two weight vectors: the top-down weight vector and the bottom-up weight vector are differently updated by a weighted sum and a fuzzy AND operation. This method effectively resolves the category proliferation problem without increasing the training epochs in noisy environments.