 Discrete memristor applied to construct neural networks with homogeneous and heterogeneous coexisting attractorsQiang Lai and Liang Yang Chaos, Solitons & Fractals, 2023, vol. 174, issue C Abstract: Memristors are widely used to simulate the effects of electromagnetic radiation on neurons or as synapses to simulate excitation and inhibition between neurons. This paper constructs discrete neural network models (DNNMs) in three scenarios: without discrete memristor, discrete memristor simulate electromagnetic radiation stimulation, and discrete local active memristor simulate synapses. Research reveals that the DNNM without fixed points under electromagnetic radiation stimulation, which in turn induces hidden hyperchaotic attractors and also observes the existence of infinite coexisting homogeneous attractors. Meanwhile, the DNNM can generate heterogeneous coexisting attractors after introducing a memristor to mimic synapses. Numerical results show that discrete memristors can induce the DNNM to develop more complex chaotic dynamics. In addition, a hardware platform is designed to validate the numerical results, and the DNNMs are applied to construct the pseudo-random number generator (PRNG). Keywords: Discrete memristor; Local active; Discrete neural network model; Hidden attractors; Hyperchaos; Coexisting attractors; Microcontroller (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007087 DOI: 10.1016/j.chaos.2023.113807 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
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