Necessary and Sufficient Condition for Absolute Exponential Stability of Hopfield-Type Neural Networks
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概要
- 論文の詳細を見る
A main result in this paper is that for a Hopfield-type neural circuit with a symmetric connection matrix T, the negative semidefiniteness of T is a necessary and sufficient condition for absolute exponential stability. While this result extends one of absolute stability in Forti, et al. [1], its proof given in this paper is simpler, which is completed by an approach different from one used in Forti et al. [1]. The most significant consequence is that the class of neural networks with negative semidefinite matrices T is the largest class of symmetric networks that can be employed for embedding and solving optimization problem with global exponential rate of convergence to the optimal solution and without the risk of spurious responses.
- 社団法人電子情報通信学会の論文
- 1996-07-25
著者
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Yamaguchi Toru
Department of Ethnology and Archaeology, Keio University
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Liang Xue-bin
Department Of Computer Science Fudan University
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Liang Xue-bin
Department Of Information Science Faculty Of Engineering Utsunomiya University:department Of Compute
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Yamaguchi Toru
Department Of Ethnology And Archaeology Keio University
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