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

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.

Automated summary

In this article, the global exponential stability problem of delayed Cohen-Grossberg inertial neural networks is addressed by constructing a new innovative Lyapunov functional. The proposed method, together with two different control schemes and the inequality technique, analyzes the stability of the considered second-order inertial neural networks. The dynamical behavior of the networks in this study is novel and different from the traditional reduced-order method through variable substitution. The simpler inequalities in the proposed method help achieve stability criteria in a more straightforward way compared to existing results. A numerical example is provided to validate the efficiency of the proposed method.