Exponential stability analysis of uncertain stochastic neural networks with multiple delays

This paper addresses the stability analysis problem for stochastic neural networks with parameter uncertainties and multiple time delays. The delays are time varying, and the parameter uncertainties are assumed to be norm bounded. A sufficient condition is derived such that for all admissible uncertainties, the considered neural network is globally exponentially stable in the mean square. The stability criterion is formulated by means of the feasibility of a linear matrix inequality (LMI), which can be easily checked in practice. Finally, a numerical example is provided to illustrate the proposed result.