Dynamic Analysis and Exponential Synchronization Control of Star-Shaped Memristive Neural Networks

This paper presents a study on the dynamic analysis of a star-shaped memristive neural network (SMNN) incorporating heterogeneous activation functions. A memristive Hopfield neural network model is analyzed for its dynamic behaviors. This network model shows rich dynamics. Finally, we explore exponential synchronization control for the SMNN. A feedback controller is designed to synchronize the network's behavior, ensuring exponential stability and reducing error in the system.