Multiple attractors and chaos synchronization of memristor-based Hopfield neural networks
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Qun Chen
,
Jianhao Li
,
Bo Li
Abstract
This study investigates the dynamic characteristics and synchronisation abilities of Hopfield neural networks (HNNs) that are augmented by memristor technology. Memristors are novel electronic devices that can retain information concerning their previous resistance states, enabling them to function as synapses within neural networks. Substituting traditional synapses in an HNN with memristors yields an innovative chaotic model within the memristor-based HNN framework that can support single- and double-scroll attractors. The model’s intricate dynamic behaviour, encompassing aspects such as multistability and the coexistence of attractors, was analysed. The practical validation of this concept was carried out via circuit construction. On the basis of this model, we designed a sliding mode control system, achieving fixed-time synchronisation and laying the groundwork for future applications having this feature, such as image encryption.
Funding source: 111 Project
Award Identifier / Grant number: Grant B17040
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 62476257, 62173130, 62073302
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Research ethics: This study does not involve any animals or plants, and there are no ethical risks associated with it.
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Informed consent: Not applicable.
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Author contributions: All authors contributed to all sections of the paper.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare that they have no conflict of interest.
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Research funding: This study is supported by National Natural Science Foundation of China (Grant Nos. 62476257, 62173130, 62073302), and the 111 Project under Grant B17040.
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Data availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Gronwall’s inequality and stability analysis of nonlinear fractional difference equations
- Dynamical behaviors and probability density function of a stochastic multi-strain SEIR model with nonlinear incidence
- Multiple attractors and chaos synchronization of memristor-based Hopfield neural networks
- An implicit graded mesh-based numerical scheme for multi-term time fractional nonlinear Fisher’s equation
Articles in the same Issue
- Frontmatter
- Research Articles
- Gronwall’s inequality and stability analysis of nonlinear fractional difference equations
- Dynamical behaviors and probability density function of a stochastic multi-strain SEIR model with nonlinear incidence
- Multiple attractors and chaos synchronization of memristor-based Hopfield neural networks
- An implicit graded mesh-based numerical scheme for multi-term time fractional nonlinear Fisher’s equation
