Abstract
A class of discrete-time stochastic switched static neural networks model is presented with the introduction of randomly occurring nonlinearities and stochastic delay. The mean square exponential stability is investigated for such kind of neural networks. In terms of linear matrix inequality (LMI) approach, a delay-dependent stability criterion is derived for the considered neural networks via a Lyapunov–Krasovskii functional. An example with simulation results is given to illustrate the effectiveness of the theoretical result.
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