Four-scroll attractor on the dynamics of a novel Hopfield neural network based on bi-neurons without bias current
Heliyon, ISSN: 2405-8440, Vol: 8, Issue: 10, Page: e11046
2022
- 15Citations
- 5Captures
- 1Mentions
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Metrics Details
- Citations15
- Citation Indexes15
- 15
- CrossRef10
- Captures5
- Readers5
- Mentions1
- News Mentions1
- 1
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Studies from University of Dschang Have Provided New Data on Science and Technology (Four-scroll attractor on the dynamics of a novel Hopfield neural network based on bi-neurons without bias current)
2022 NOV 08 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Research findings on science and technology are discussed in
Article Description
The dynamics of a neural network under several factors (bias current and electromagnetic induction effect) are recently used to simulate activities of the brain under different excitation. In this paper, we introduce a novel Hopfield neural network (HNN) based on two neurons with a memristive synaptic weight connected between neuron one and two based of flux controlled memristor recently proposed by Hua M. et al., in 2022. Using analysis tools, we proved that this model can develop rich dynamical characteristics such as various number of equilibrium points when the parameters are varied, four-scroll attractors, transient chaos, multistability of more than three different attractors and intermittency chaos phenomenon are reported. Moreover, when increasing a synaptic weight, the model shows bursting oscillations phenomenon. To obtain the normal state of the brain, the control of multistability to a strange monostable state is carry out. Finally, microcontroller implementation of the model is considered to verify the numerical analysis.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2405844022023349; http://dx.doi.org/10.1016/j.heliyon.2022.e11046; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85140218188&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/36303901; https://linkinghub.elsevier.com/retrieve/pii/S2405844022023349; https://dx.doi.org/10.1016/j.heliyon.2022.e11046
Elsevier BV
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