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Absolute Exponential Stability of Neural Networks with Asymmetric Connection Matrices
Xue-Bin LIANG Toru YAMAGUCHI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1997/08/25
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Neural Networks
neural networks, asymmetric connection matrices, absolute exponential stability time constant, global exponential convergence rate,
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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.