Global asymptotic synchronization of inertial memristive Cohen-Grossberg neural networks with proportional delays

In this article, the global asymptotic synchronization (GAS) using inertial memris-tive Cohen-Grossberg neural networks (IMCGNNs) with proportional delays (PDs) as drive-response systems is studied. By utilizing differential inclusion theory (DIT) and appropriate variable transformation, the studied systems can be converted to first-order differential systems. Both the feedback controller and the adaptive controller are designed, which are easy to implement in hardware. By constructing Lyapunov functionals and using mean value inequality analysis skills, two GAS criteria of the studied systems are gained, which are embodied in the form of algebraic inequalities and are easy to verify. Ultimately, the numerical examples with simulations are applied to sustain the obtained consequences.(c) 2023 Published by Elsevier B.V.