Global exponential stability of neutral-type Cohen-Grossberg neural networks with multiple time-varying neutral and discrete delays

This paper studies the global exponential stability of neutral-type Cohen-Grossberg neural networks (NTCGNNs) with multiple time-varying discrete and neutral delays. Since the system model can not be expressed in the form of a vector-matrix,some methods and techniques for stability analysis of the vector-matrix models will not be available. First, a novel criterion is proposed to ensure the existence and uniqueness of equilibrium point (EP) of the NTCGNNs under consideration. Then, with the purpose of ensuring the global exponential stability of the unique EP, a new Lyapunov-Krasovskii functional is constructed to obtain novel stability criteria. Several representative numerical examples are used to demonstrate the applicability of the obtained stability conditions, and its advantages over the existing ones. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper studies the global exponential stability of neutral-type Cohen-Grossberg neural networks that have multiple time-varying discrete and neutral delays. A novel criterion is proposed to ensure the existence and uniqueness of equilibrium point, and a new Lyapunov-Krasovskii functional is constructed to achieve global exponential stability.