A Triple-Memristor Hopfield Neural Network With Space Multistructure Attractors and Space Initial-Offset Behaviors

Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This article proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural ne...

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Vydané v:	IEEE transactions on computer-aided design of integrated circuits and systems Ročník 42; číslo 12; s. 4948 - 4958
Hlavní autori:	Lin, Hairong, Wang, Chunhua, Yu, Fei, Hong, Qinghui, Xu, Cong, Sun, Yichuang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.12.2023
Predmet:	Liapunov exponents Memristors Neural networks Poincare maps Pseudorandom
ISSN:	0278-0070, 1937-4151
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Abstract	Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This article proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multistructure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multistructure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multistructure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudorandom number generator to explore the potential application of the proposed TM-HNN.
AbstractList	Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This article proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multistructure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multistructure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multistructure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudorandom number generator to explore the potential application of the proposed TM-HNN.
Author	Wang, Chunhua Hong, Qinghui Lin, Hairong Xu, Cong Sun, Yichuang Yu, Fei
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SubjectTerms	Liapunov exponents Memristors Neural networks Poincare maps Pseudorandom
Title	A Triple-Memristor Hopfield Neural Network With Space Multistructure Attractors and Space Initial-Offset Behaviors
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