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ON THE ASYMPTOTIC BEHAVIOR OF HOPFIELD NEURAL NETWORK WITH PERIODIC INPUTS 被引量:1

ON THE ASYMPTOTIC BEHAVIOR OF HOPFIELD NEURAL NETWORK WITH PERIODIC INPUTS
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摘要 Without assuming the boundedness and differentiability of the nonlinear activation functions, the new sufficient conditions of the existence and the global exponential stability of periodic solutions for Hopfield neural network with periodic inputs are given by using Mawhin's coincidence degree theory and Liapunov's function method. Without assuming the boundedness and differentiability of the nonlinear activation functions, the new sufficient conditions of the existence and the global exponential stability of periodic solutions for Hopfield neural network with periodic inputs are given by using Mawhin's coincidence degree theory and Liapunov's function method.
出处 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2002年第12期1367-1373,共7页 应用数学和力学(英文版)
关键词 Hopfield neural network periodic solution global exponential stability coincidence degree Liapunov's function Hopfield neural network periodic solution global exponential stability coincidence degree Liapunov's function
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