Exponential Stability of Matrix-Valued BAM Neural Networks with Time-Varying Delays

Abstract

Matrix-valued BAM neural networks are a generalization of real-valued BAM neural networks, for which the states, weights, and outputs are square matrices. This paper gives a sufficient criterion expressed in terms of linear matrix inequalities, for which the equilibrium point of these networks with time-varying delays is exponentially stable. A numerical example is provided to demonstrate the effectiveness of the proposed criterion.