Optimal Design of Hopfield-Type Associative Memory by Adaptive Stability-Growth Method
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概要
- 論文の詳細を見る
An adaptive stability-growth (ASG) learning algorithm is proposed for improving, as much as possible, the stability of a Hopfield-type associative memory. While the ASG algorithm can be used to determine the optimal stability instead of the well-known minimum-overlap (MO) learning algorithm with sufficiently large lower bound for MO value, it converges much more quickly than the MO algorithm in real implementation. Therefore, the proposed ASG algorithm is more suitable than the MO algorithm for real-world design of an optimal Hopfield-type associative memory.
- 社団法人電子情報通信学会の論文
- 1998-01-25
著者
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Liang Xue-bin
Department Of Computer Science Fudan University
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Yamaguchi Toru
Department Of Ethnology And Archaeology Keio University
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Yamaguchi Toru
Department of Electronic System Engineering, Tokyo Metropolitan Institute of Technology
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