Less Conservative Stability Criteria for a Class of Nonlinear Stochastic Hopfield Neural Networks with Time-varying Delays
Tieyan Zhang, Yan Hui Zhao, Xiangpeng Xie, Meng Dong · Applied Mathematics & Information Sciences · 2013
In this paper, the problem of stability analysis of nonlinear stochastic Hopfield neural networks(FHNNs) with time-varying delays is investigated by using the Takagi-Sugeno(T-S) approach. Combined with both the fuzzy relaxed technique and an improved free-weighting matrix approach with weighting-dependent Lagrange multipliers, less conservative stability criteria is proposed via the Lyapunov-Krasovskii functional approach. Furthermore, related algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis and the obtained stability criteria is in terms of Linear matrix inequalities. Finally, an illustrative example shows less conservatism of the proposed approaches.