An improved delay-partitioning approach to stability criteria for generalized neural networks with interval time-varying delays

Abstract

This paper deals with the problem of stability analysis for generalized delayed neural networks with interval time-varying delays based on the delay-partitioning approach. By constructing a suitable Lyapunov–Krasovskii functional with triple- and four-integral terms and using Jensen’s inequality, Wirtinger-based single- and double-integral inequality technique and linear matrix inequalities (LMIs), which guarantees asymptotic stability of addressed neural networks. This LMI can be easily solved via convex optimization algorithm. The novelty of this paper is that the consideration of a new integral inequalities and Lyapunov–Krasovskii functional is shown to be less conservatism, and it takes fully the relationship between the terms in the Leibniz–Newton formula within the framework of LMIs. Moreover, it is assumed that the lower bound of the time-varying delay is not restricted to be zero. Finally, several interesting numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over well-known examples existing in recent literature.