TL;DR
A discrete memristor with synaptic plasticity characteristics is designed and incorporated into a two-dimensional discrete Hopfield neural network to establish a discrete memristive Hopfield neural network (DMHNN).
Memristive Hopfield neural networks (HNNs) have been widely explored for chaotic signal generation and data encryption owing to their rich dynamical behaviors. However, most existing memristive HNN models suffer from limited controllability of the number of coexisting attractors, insufficient dimensional complexity of...
In this paper, a novel three-dimensional discrete memristive chaotic system is proposed. By embedding a single discrete memristor into a three-dimensional mapping structure, the system achieves hyperchaotic dynamics with a concise architecture. Theoretical analysis indicates that the equilibrium points of the system ex...
Unlike conventional models that typically rely on a single memristive synapse, this study uniquely proposes a novel 5D fractional-order memristive Hopfield neural network (FOMHNN) modulated by dual memristors to simultaneously emulate internal synaptic plasticity and external electromagnetic radiation effects in brain-...
With the rapid growth of multimedia data transmission over potentially insecure channels, secure video encryption has become increasingly important. A five-dimensional discrete memristive cellular neural network is constructed by embedding voltage-controlled and magnetically-controlled discrete memristors into a three-...
A comprehensive dynamical analysis of a novel Hopfield neural network in which two distinct memristors are introduced as synaptic connections demonstrates the robustness of the proposed model in secure communication applications and the potential of the proposed memristive HNN for secure communication and chaos-based a...
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