Exponential Stabilization of Phase-Change Inertial Neural Networks With Time-Varying Delays

Phase-change memory (PCM) is a novel type of nonvolatile memory and offers low power consumption, high integration, and significant plasticity, making it suitable for neural synapses. In this article, we investigate the global exponential stabilization (GES) of phase-change inertial neural networks (PCINNs) with discrete and distributed time-varying delays. Initially, a piecewise equation is established to model the electrical conductivity of PCM. Based on this, we use PCM to simulate neural synapses, and a class of PCINNs with discrete and distributed time-varying delays is formulated. A continuous state feedback controller is designed to obtain the ${\boldsymbol {\rho} {\textrm {th}}({\rho \ge 1})}$ moment GES conditions of PCINNs in the Filippov sense by using differential inclusion theory, comparison strategies, and inequality techniques. Additionally, the global exponential stability conditions of phase-change Hopfield neural networks are obtained, expressed in the form of an M-matrix. Finally, three simulation examples are provided to verify the effectiveness of the theoretical results.