Novel stability criteria of Cohen–Grossberg neural networks with time‐varying delays

Abstract

SUMMARYIn this paper, a class of Cohen-Grossberg neural networks with time-varying delays is investigated. Based on several new Lyapunov-Krasovskii functionals, by employing the homeomorphism mapping principle, the Halanay inequality, a nonlinear measure approach and linear matrix inequality techniques, several delay-independent sufficient criteria are obtained for the existence, uniqueness and globally exponential stability of considered neural networks. Without assuming the boundedness and monotonicity of activation functions, the obtained conditions generalize some previous results in the literature. Two examples are also given to show the less conservativeness of the obtained conditions.

Stability and synchronization of directed complex dynamical networks with random packet loss: the continuous‐time case and the discrete‐time case

“…For example, Yu et al [25][26][27] investigated the second-order and general higher-order distributed consensus protocols and reliable directed networks with nonlinear dynamics. There are also numerous works [10][11][12][13][22][23][24] on the network with delays. However, it is often the case that information transmission may fail and some of the data may be lost, because of either internal causes such as oversaturated network links or external causes such as noise interference and artificial attacks.…”