Global Exponential Stability of Inertial Cohen–Grossberg Neural Networks with Time-Varying Delays via Feedback and Adaptive Control Schemes: Non-reduction Order Approach

Abstract

In this article, the problem is dealt for the global exponential stability of delayed Cohen–Grossberg inertial neural networks (CGINNs) by constructing a new innovative Lyapunov functional instead of the traditional reduced-order method. The newly constructed Lyapunov functional together with two different control schemes and the inequality technique, analyze the global exponential stability for the considered second-order inertial neural networks (INNs). The dynamical behavior of CGINNs in the present study is new and different from the reduced-order method through variable substitution. The simpler inequalities in the proposed method help to achieve the stability criteria of CGINNs in a easier way as compared to the existing results. Finally, a numerical example is presented to validate the efficiency of the proposed method. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.