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基于无偏置电流双神经元的新型霍普菲尔德神经网络动力学中的四-scroll吸引子

Four-scroll attractor on the dynamics of a novel Hopfield neural network based on bi-neurons without bias current.

作者信息

Boya Bertrand Frederick Boui A, Kengne Jacques, Djuidje Kenmoe Germaine, Effa Joseph Yves

机构信息

Research Unit of Automation and Applied Computer (UR-AIA), Electrical Engineering Department of IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon.

Laboratory of Mechanics, Department of Physics, Faculty of Science, University of Yaoundé 1, Yaoundé, Cameroon.

出版信息

Heliyon. 2022 Oct 14;8(10):e11046. doi: 10.1016/j.heliyon.2022.e11046. eCollection 2022 Oct.

Abstract

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.

摘要

最近,神经网络在多种因素(偏置电流和电磁感应效应)下的动力学被用于模拟大脑在不同刺激下的活动。在本文中,我们基于两个神经元引入了一种新型霍普菲尔德神经网络(HNN),这两个神经元之间通过基于华M等人于2022年最近提出的磁通控制忆阻器的忆阻突触权重相连。使用分析工具,我们证明了该模型在参数变化时能够展现出丰富的动力学特性,如不同数量的平衡点、四涡卷吸引子、瞬态混沌、三种以上不同吸引子的多稳定性以及间歇性混沌现象。此外,当增加一个突触权重时,该模型会出现爆发振荡现象。为了获得大脑的正常状态,对多稳定性进行控制使其达到奇异单稳态。最后,考虑对该模型进行微控制器实现以验证数值分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4371/9593194/b93b775cc5b0/gr001.jpg

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