A Memristive Synapse Control Method to Generate Diversified Multistructure Chaotic Attractors

Due to the synapse-like nonlinearity and memory characteristics, memristor is often used to construct memristive neural networks with complex dynamical behaviors. However, memristive neural networks with multistructure chaotic attractors have not been found yet. In this article, a novel method for d...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2023-03, Vol.42 (3), p.942-955
Main Authors: Lin, Hairong, Wang, Chunhua, Xu, Cong, Zhang, Xin, Iu, Herbert H. C.
Format: Article
Language:English
Subjects:
Analog circuits
Behavioral sciences
Chaotic communication
Circuit design
Circuit implementation
Control methods
Design parameters
Encryption
extreme multistability
Hopfield neural network (HNN)
Hysteresis
image encryption
memristor
Memristors
multistructure attractor
Neural networks
Neurons
Permutations
Synapses
Topology
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Summary:Due to the synapse-like nonlinearity and memory characteristics, memristor is often used to construct memristive neural networks with complex dynamical behaviors. However, memristive neural networks with multistructure chaotic attractors have not been found yet. In this article, a novel method for designing multistructure chaotic attractors in memristive neural networks is proposed. By utilizing a multipiecewise memristive synapse control in a Hopfield neural network (HNN), various complex multistructure chaotic attractors can be produced. Theoretical analysis and numerical simulation demonstrate that multiple multistructure chaotic attractors with different topologies can be generated by conducting the memristive synapse-control in different synaptic coupling positions. Differing from traditional multiscroll attractors, the generated multistructure attractors contain multiple irregular shapes instead of simple scrolls. Meanwhile, the number of structures can be easily controlled with the memristor control parameters. Furthermore, we design a module-based analog memristive neural network circuit and the arbitrary number of multistructure attractors can be obtained by selecting corresponding control voltages. Finally, based on the memristive HNNs, a novel image encryption cryptosystem with a permutation-diffusion structure is designed and evaluated, exhibiting its excellent encryption performances, especially the extremely high key sensitivity.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2022.3186516