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Improvement of Fuzzy ARTMAP Performance in Noisy Input Environment Using Weighted-Average Learning
Jae Sul LEE Chang Joo LEE Choong Woong LEE
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1997/05/25
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Neural Networks
fuzzy ARTMAP, weighted average, pattern recognition, weight vector, category proliferation,
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An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.