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

• Published in: Neural processing letters Vol. 55; no. 4; pp. 4347 - 4363
• Main Authors: Singh, Sunny, Kumar, Umesh, Das, Subir, Cao, Jinde
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2023; Springer Nature B.V
• Subjects: Adaptive control; Artificial Intelligence; Behavior; Complex Systems; Computational Intelligence; Computer Science; Dynamic stability; Neural networks; Neurobiology; Neurons; Neurosciences; Stability analysis; Stability criteria; Time varying control; Variables; Time delay; Exponential stabiliy; Inertial Cohen–Grossberg type neural networks; Feedback and adaptive control
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-022-11044-9

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.