Abstract
ABSTRACTIn this paper, the exponential stability problem is considered for a class of hysteretic Hopfield neural networks with stochastic disturbances. The hysteretic nonlinearities are characterized by a Lipschitz-type constraint where the internal parameters of the hysteretic function are reflected. By resorting to Lyapunov function approach and stochastic analysis, a sufficient condition has been obtained under which the underlying hysteretic Hopfield neural network is exponentially stable in the mean square. The obtained condition is expressed in terms of linear matrix inequalities (LMIs) which can be easily checked via the Matlab toolbox. Finally, an illustrative example is provided to show the effectiveness of the results derived in this paper.
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