A novel adaptive control design for exponential stabilization of memristor-based CVNNs with time-varying delays using matrix measures

The present study introduces a new adaptive control framework that aims to attain exponential stability in complex-valued neural network systems utilizing memristors while accounting for time-varying delays. The control issues in systems of this nature are mostly attributed to the presence of memristors and time-varying latency. To overcome these challenges and achieve stabilization outcomes, a methodology is employed that integrates adaptive control approaches inside a matrix-based framework. This study employs Lyapunov's stability theory to establish exponential stabilization conditions and conduct convergence analysis. The efficacy of the suggested control algorithm in achieving exponential stabilization and robustness under varied delays is demonstrated through numerical simulations.