On the Absolute Exponential Stability of Neural Networks with Globally Lipschitz Continuous Activation Functions
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概要
- 論文の詳細を見る
In this letter, we obtain the absolute exponential stability result of neural networks with globally Lipschitz continuous, increasing and bounded activation functions under a sufficient condition which can unify some relevant sufficient ones for absolute stability in the literature. The obtained absolute exponential stability result generalizes the existing ones about absolute stability of neural networks. Moreover, it is demonstrated, by a mathematically rigorous proof, 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 presented to illustrate the analysis results.
- 社団法人電子情報通信学会の論文
- 1997-06-25
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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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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