NOVEL STABILITY CRITERIA FOR NONLINEAR STOCHASTIC HOPFIELD NEURAL NETWORKS WITH TIME-VARYING DELAYS

摘要

This paper investigates the stability analysis of nonlinear stochastic Hopfield neural networks (NSHNNs) with time-varying delays which are described by a Takagi-Sugeno (T-S) fuzzy model. By using an improved homogeneous matrix polynomials technique, novel convergent stability criteria are proposed. More importantly, as the selected design parameter becomes larger, less conservative stability criteria will be obtained since more algebraic property of the fuzzy weighting functions in the unit simplex can be utilized to deal with stability analysis. Finally, an illustrative example is provided to show the efficiency of the proposed approach.