New integral inequalities for exponential stability of neural networks with time-varying delay

Abstract

This paper addresses the exponential stability of delayed neural networks by extending existing integral inequalities. Specifically, we consider a refined Lyapunov-Krasovskii function that incorporates decay rate terms and employs the new integral inequalities containing additional information on the delay-dependent terms to derive less conservative stability criteria for delayed neural networks. Numerical simulations validate the effectiveness of the proposed approach, demonstrating its potential for practical implementation in the control design of delayed neural networks and highlighting its advantages over traditional methods.