Global exponential stability of inertial Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays
Hongjun Qiu, Fanchao Kong · International Journal of Control · 2021
This paper aims to investigate the global exponential stability of a class of inertial Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays. By constructing a modified delay-dependent Lyapunov-Krasovskii functional, delay-dependent criteria stated with simple algebraic inequalities are given in order to ensure the global exponential stability for the addressed neural network model. In sharp contrast to the existed reduced order method used to and delay-independent criteria derived for the neural networks with inertial terms, the model proposed and results established of this paper are more general and rigorous. Finally, numerical examples with simulations are presented to illustrate the main results.