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Memristors are widely used to construct multi-scroll/wing chaotic systems with complex dynamics. However, the generation of a multi-scroll/wing attractor is typically not induced by the memristor but depends on other nonlinear functions in the system, which does not take advantage of the unique features of the memristor for chaos-based applications. To address this issue, the present paper introduces a memristor coupling (MC) method to construct a novel memristive Sprott A system (MSAS) through coupling a flux-controlled memristor with multi-piecewise linear memductance into the chaotic Sprott A system. From theoretical analysis and numerical simulations, the MSAS is shown to be able to generate any number of multi-type hidden attractors, including multi-one-scroll, multi-double-scroll and multi-double-wing hidden attractors. In addition, it has two kinds of multistabilities, that is, heterogeneous multistability and homogeneous multistability. Based on these unique properties, different numbers of coexisting heterogeneous hidden attractors and coexisting homogeneous hidden attractors are derived respectively by switching the memristor initial states. These interesting dynamical properties are comprehensively investigated using nonlinear analysis tools. Furthermore, hardware experiments are implemented to demonstrate the feasibility of the MSAS and the effectiveness of the MC method. Finally, a new pseudo-random number generator (PRNG) is proposed to explore the practical applications of the MSAS. Performance evaluation results verify the high-quality randomness of the designed PRNG. [ABSTRACT FROM AUTHOR]

In this paper, implementation of memristive neural network with full-function Pavlov associative memory is designed based on a proposed associative memory rule. The designed neural network can well perform the Pavlov associative memory in the network with at least three interconnected neurons. This neural network and the associative memory rule that is partly based on spike-rate-dependent plasticity (SRDP) protocol are inspired by the famous Pavlov’s dog-experiment that demonstrated the interrelation between the “sight of food” and the “ringing.” Besides the learning activity, the proposed network can also perform two kinds of forgetting activities after the learning process is completed: on one hand, when the salivation neuron is stimulated by the food neuron alone, after a period of time, the ring neuron can no longer trigger the salivation neuron; on the other hand, when the salivation neuron is stimulated by the ring neuron alone, at first the salivation neuron can be triggered but after the salivation neuron realizes that the “ringing” is not associated with “food,” the salivation neuron will not be triggered any longer. How to integrate the proposed network into large scale memristive neural network with multiple associations is also introduced. Simulations results demonstrate the correctness of the designs. [ABSTRACT FROM AUTHOR]