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• W2093655427 abstract "In this paper, we study the global exponential stability of time-delayed stochastic bidirectional associative memory neural networks with impulses and Markovian jumping parameters. A generalized activation function is considered, and traditional assumptions on the boundedness, monotony and differentiability of activation functions are removed. We obtain a new set of sufficient conditions in terms of linear matrix inequalities, which ensures the global exponential stability of the unique equilibrium point for stochastic BAM neural networks with impulses. The Lyapunov function method with the Ito differential rule is employed for achieving the required result. Moreover, a numerical example is provided to show that the proposed result improves the allowable upper bound of delays over some existing results in the literature." @default.
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• W2093655427 date "2010-09-29" @default.
• W2093655427 modified "2023-10-03" @default.
• W2093655427 title "New exponential stability criteria for stochastic BAM neural networks with impulses" @default.
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• W2093655427 doi "https://doi.org/10.1088/0031-8949/82/04/045802" @default.
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