Stochastic finite-time stability analysis of Markovian jumping neural networks with mixed time delays

Abstract

The stochastic finite-time stability is studied in this paper for Markovian jumping neural networks with discrete and distributed delays. By defining a proper stochastic Lyapunov functional with mode-dependent Lyapunov matrices, a sufficient condition is derived such that the delayed Markovian jumping neural network under consideration is stochastically finite-time stable with respect to prescribed scalars. The stability criterion is delay- and mode-dependent, and can be readily checked by resorting to available algorithms. Two numerical examples are finally provided to show the application of the developed theory.