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On the Global Asymptotic Stability Independent of Delay of Neural Networks
Xue-Bin LIANG Toru YAMAGUCHI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1997/01/25
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Neural Networks
neural networks, global asymptotic stability, independent of delay,
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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.