Memristor-cascaded hopfield neural network with attractor scroll growth and STM32 hardware experiment

Abstract

This paper proposes a memristor-cascaded Hopfield neural network (MC-HNN), which is constructed by directly cascading two bi-neuron Hopfield neural networks (HNNs) using a memristor synapse. With its mathematical model, the stability of line equilibrium set is analyzed, and two representative chaotic attractors are revealed. On this basis, the multi-scroll chaotic attractor (MSCA) with scroll growth over time is investigated. The results manifest that MC-HNN can not only generate MSCA with unidirectional and bidirectional scroll-growths, but also produce initial-offset coexisting attractors. In particular, the generated MSCA only involves the line equilibrium set, so Shil'nikov's theorem does not apply in this case. Finally, STM32 hardware board is developed, and the numerically simulated results are captured experimentally by oscilloscope.