On the Global Asymptotic Stability Independent of Delay of Neural Networks
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概要
- 論文の詳細を見る
Recurrent neural networks have the potential of performing parallel computation for associative memory and optimization, which is realized by the electronic implementation of neural networks in VLSI technology. Since the time delays in real electronic implementation of neural networks are unavoidably encountered and they can cause systems to oscillate, it is thus practically important to investigate the qualitative properties of neural networks with time delays. In this paper, a class of sufficient conditions is obtained, under which neural networks are globally asymptotically stable independent of time delays.
- 社団法人電子情報通信学会の論文
- 1997-01-25
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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