Absolute Exponential Stability of Neural Networks with Asymmetric Connection Matrices
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概要
- 論文の詳細を見る
In this letter, the absolute exponential stability result of neural networks with asymmetric connection matrices is obtained, which generalizes the existing one about absolute stability of neural networks, by a new proof approach. It is demonstrated that the network time constant is inversely proportional to the global exponential convergence rate of the network trajectories to the unique equilibrium. A numerical simulation example is also given to illustrate the obtained analysis results.
- 社団法人電子情報通信学会の論文
- 1997-08-25
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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