A direct parameterized approach to global exponential stability of neutral-type Cohen-Grossberg neural networks with multiple discrete and neutral delays

For a class of neutral-type Cohen-Grossberg neural networks with multiple discrete and neutral delays, the existence and uniqueness of equilibrium point are derived by the homeomorphism mapping theory between topology spaces. By proposing a direct parameterized approach that is based on a parameterized estimation of solutions, it is the first time to investigate a sufficient condition guaranteeing that the unique equilibrium point is globally exponentially stable. The stability condition contains only some very simple inequalities, which is easily solved by applying the toolbox YALMIP of MATLAB.Three numerical examples in literature are employed to demonstrate the effectiveness of the obtained criterion over the previously achieved ones. In addition, the proposed approach is different from the popular linear matrix inequality approach, since no Lyapunov-Krasovskii functional is required. Therefore, the obtained criterion can be evaluated as an important contribution to the stability issue of the considered neural networks. It is potential that the proposed approach is applied to stability issues of various types of neural networks. (C) 2021 Published by Elsevier B.V.