New Results on Input-to-State Stability of Memristor-Based Inertial Neural Networks

The main focus of this study is the analysis of input-to-state stability problem for inertial neural networks with delays utilizing memristors. By utilizing techniques such as Lyapunov stability theory, inequality techniques, and nonsmooth analysis, we establish sufficient conditions through the approach of variable substitution. By employing the reduced order method, we transform the second-order system to a first-order system, allowing us to obtain conditions that are both dependent and independent of time-delays. Furthermore, the algebraic criteria introduced in this study are straightforward to verify. To illustrate the usefulness of the proposed criterion, we provide a numerical example.