A novel memristive synapse-coupled ring neural network with countless attractors and its application

Abstract

This paper presents a novel memristive synapse-coupled ring neural network (MSCRNN) through introducing a nonvolatile memristor into a three-neuron Hopfield neural network connected by a unidirectional ring topology. Complex dynamics relying on control parameters and initial states is thoroughly explored using numerical analysis techniques. Numerical analyses show that the MSCRNN not only exhibits bistability, tristability, but also in particular evolves an intriguing phenomenon known as homogeneous multistability, characterized by the emergence of an infinite number of homogeneous coexisting attractors triggered by the memristor initial states. In addition, a hardware test platform based on the CH32 microcontroller is built to experimentally validate these numerical findings. Finally, a new pseudorandom number generator is developed taking advantage of memristor initial-regulated chaotic sequences derived from the MSCRNN. Performance analysis outcomes indicate that these chaotic sequences possess the capability to generate pseudorandom numbers demonstrating exceptional randomness, rendering them highly advantageous for utilization in various chaos-based engineering applications.