Robust stability analysis of stochastic switched neural networks with parameter uncertainties via state-dependent switching law

Abstract The problem of robust stability analysis for a class of stochastic switched neural networks (SSNNs) with time-varying parametric uncertainties is investigated in this paper. Some sufficient conditions are derived to guarantee the robust global asymptotical stability in mean square for the uncertain SSNNs by using state-dependent switching (SDS) method. It is shown that the robust stability of uncertain SSNNs composed of all unstable subnetworks can be achieved by using the designed SDS law. Moreover, the proposed sufficient conditions can be easily checked in terms of linear matrix inequalities (LMIs) for conveniently using Matlab toolbox. A numerical example is provided to demonstrate the effectiveness of the proposed SDS law.