Delay-dependent asymptotical stability analysis of nonlinear delay neural networks

Abstract

In the note, the global asymptotic stability of nonlinear cellular neural networks with constant delay is studied. At first, a transformation is made the nonlinear neural networks into the linear neural networks. Then the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed to investigate the problem. A novel sufficient condition is derived that is less conservative than the ones reported so far in the literature. Numerical examples illustrate the effectiveness of the method and improvement over some existing methods.