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• W52673114 abstract "If the matrix measure of connection weight of Hopfield-type continuous feedback neural network is less than the reciprocal of maximal product of resistance and gain constants, then the network system is globally and exponentially stable. The above reciprocal is a sharp upper bound of matrix measure of connection weight which guarantees that the above conclusion holds. The above result answers partially the open problem proposed by Vidyasagar recently, i. e whether neural network with nearly symmetric connection weight can exhibit limit cycles. The relation between the network time constant and the global exponential convergence rate is pointed out, and application to optimization computation of our results is also given." @default.
• W52673114 created "2016-06-24" @default.
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• W52673114 date "1995-06-20" @default.
• W52673114 modified "2023-09-26" @default.
• W52673114 title "Global exponential stability of Hopfield-type neural network and its applications" @default.
• W52673114 doi "https://doi.org/10.1360/ya1995-38-6-757" @default.
• W52673114 hasPublicationYear "1995" @default.
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