Algebra criteria for global exponential stability of multiple time-varying delay Cohen-Grossberg neural networks

This paper aims at establishing global exponential stability criteria for multiple time -varying delay Cohen-Grossberg neural networks (CGNNs). The considered network models cannot be expressed as the vector-matrix form, which yields that many methods in lit-erature are unavailable. By constructing novel Lyapunov-Krasovskii functionals, two novel algebraic criteria guaranteeing global exponential stability of CGNNs under consideration are given. A pair of numerical examples are used to explain the effectiveness of the ob-tained algebra criteria relative to the previously stability conditions. It is worth emphasiz-ing that the approach applied in this paper is applicable to CGNNs that may or may not be represented in vector-matrix form. (c) 2022 Elsevier Inc. All rights reserved.