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New exponential stability criteria for stochastic BAM neural networks with impulses. (English) Zbl 1202.93101
Summary: 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 ItĂ´ 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.
MSC:
93D05Lyapunov and other classical stabilities of control systems
82C32Neural nets (statistical mechanics)
92B20General theory of neural networks (mathematical biology)