Improvement of Fuzzy ARTMAP Performance in Noisy Input Environment Using Weighted-Average Learning
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概要
- 論文の詳細を見る
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 also increases the recognition ratio in the noisy environment.
- 社団法人電子情報通信学会の論文
- 1997-05-25
著者
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LEE Choong
Department of Anatomy and Cell Biology, College of Veterinary Medicine and BK21 Program for Veterina
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LEE Jae
Department of Metallurgy, The University of Tokyo
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Lee C
Department Of Electronics Engineering Seoul National University
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Lee Choong
Department Of Anatomy And Cell Biology College Of Veterinary Medicine And Bk21 Program For Veterinar
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Lee Chang
Dept. Of Interaction Design. Graduate School Of Techno Design Kookmin University
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Lee Chang
Dept. Of Civil Engineering University Of Seoul
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Lee Jae
Department Of Acupuncture & Moxibustion College Of Oriental Medicine
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Lee Chang
Dept. Of Elec. And Comm. Engineering Dongyang Technical College
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