Robust Sampled‐Data Synchronization of Memristor Inertial Competitive Neural Networks With Two Delay Components

ABSTRACT The current study addresses the issue of synchronization in competitive neural networks that are based on memristors and involve an inertial term, parameter uncertainty, and two delay components using sampled‐data control. To achieve synchronization, appropriate Lyapunov‐Krasovskii functionals (LKFs) are constructed, which include double and triple integral terms that capture the information of time delay cross terms. Some sufficient synchronization conditions are derived in terms of linear matrix inequalities (LMIs) using an improved reciprocally convex combination inequality and generalized free weighting matrices inequality. The effectiveness of the proposed findings is highlighted through an illustrative example, validating the robustness and reliability of the developed synchronization approach.