The Stabilization of Cohen-Grossberg BAM Neural Network System

This paper investigates the stabilization problem of a class of Cohen Grossberg BAM neural network with time-varying delays. Instead of using matrix measure methods, linear matrix inequality (LMI) methods, and integral inequality methods, a new research approach is adopted: the differential inequality method, which utilizes the properties of higher-order polynomials. By finding two local extrema points of a function and designing an appropriate controller, based on Lyapunov function theory, the asymptotic stability criterion for CG-BAMNNs is obtained. In the application of the differential inequality method, the process of finding two local extrema points and the application of the properties of higher-order polynomials are very proficient, resulting in sufficiently novel outcomes. Finally, the reasonableness and effectiveness of this approach are verified through simulation examples.