[1]王义波,闵富红,张 雯,等.忆阻FitzHugh-Nagumo神经元电路有限时间同步[J].南京师范大学学报(工程技术版),2020,20(02):007-14.[doi:10.3969/j.issn.1672-1292.2020.02.002]
　Wang Yibo,Min Fuhong,Zhang Wen,et al.Finite-time Synchronization of Memristor-Based FitzHugh-Nagumo Circuit[J].Journal of Nanjing Normal University(Engineering and Technology),2020,20(02):007-14.[doi:10.3969/j.issn.1672-1292.2020.02.002]
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忆阻FitzHugh-Nagumo神经元电路有限时间同步
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
20卷
期数:
2020年02期
页码:
007-14
栏目:
电气工程
出版日期:
2020-05-15

文章信息/Info

Title:
Finite-time Synchronization of Memristor-Based FitzHugh-Nagumo Circuit
文章编号:
1672-1292(2020)02-0007-08
作者:
王义波; 闵富红; 张 雯; 叶彪明
南京师范大学南瑞电气与自动化学院,江苏 南京 210023
Author(s):
Wang Yibo; Min Fuhong; Zhang Wen; Ye Biaoming
School of NARI Electrical and Automation,Nanjing Normal University,Nanjing 210023,China
关键词:
FitzHugh-Nagumo电路; 降维模型; 有限时间; 滑模控制
Keywords:
FitzHugh-Nagumo’s circuit; dimensionality reduction model; finite time; sliding mode control
分类号:
O415.5; TP13
DOI:
10.3969/j.issn.1672-1292.2020.02.002
文献标志码:
A
摘要:
主要研究了降维忆阻FitzHugh-Nagumo系统的非线性动力学行为以及有限时间内的多稳态同步. 首先建立系统精确降维模型,展开降维忆阻系统随不同初始状态变化的动力学行为分析. 利用分岔图和Lyapunov指数谱等分析方法,研究发现调整系统初始状态,系统呈现出多稳态现象. 通过滑动模态控制方法实现两个忆阻FitzHugh-Nagumo神经元电路有限时间内的多稳态同步. 最后,通过同步数值仿真证明了控制方法的正确性和有效性.
Abstract:
In this paper,the nonlinear dynamic behavior and multi-stable synchronization of memristor-based FitzHugh-Nagumo system for dimension reduction are studied. The accurate dimensionality reduction model of the system is first established. The dynamic behavior analysis of the dimensionality reduction memristive system with different original initial state is developed through bifurcation diagrams and Lyapunov exponent. And the multistability of the system is investigated. More importantly,the sliding mode control method is designed to achieve the finite-time synchronization of the multistable memristive neuron systems,which make two different behaviors of system synchronized. Finally,numerical simulations show the effectiveness and correctness of the sliding mode controller designed.

参考文献/References:

[1] ABRAR H A,SHUAI B,ZHEN Z,et al. Multimodal recurrent neural networks with information transfer layers for indoor scene labeling[J]. IEEE Transactions on Multimedia,2018,20(7):1656-1671.
[2]BARTSCHAT A,ALLGEIER S,BOHN S,et al. Digital image processing and deep neural networks in ophthalmology-current trends[J]. Klinische Monatsbl?tter Für Augenheilkunde,2019,236(12):1399-1406.
[3]CHO J H,PARK C G. Multiple feature aggregation using convolutional neural networks for SAR image-based automatic target recognition[J]. IEEE Geoscience and Remote Sensing Letters,2018,15(12):1882-1886.
[4]HINDMARSH J L,ROSE R M. A model of the nerve impulse using two first-order differential equations[J]. Nature,1982,296(5853):162-164.
[5]MORRIS C,LECAR H. Voltage oscillations in the barnacle giant muscle fiber[J]. Biophysical Journal,1981,35(1):193-213.
[6]HODGKIN A L,HUXLEY A F. Current carried by sodium and potassium ions through the membrane of the giant axon of Loligo[J]. Journal of Physiology,1952,116(4):449-72.
[7]FITZHUGH R. Threshold and plateau in the Hodgkin-Huxley nerve equations[J]. Journal General Physiology,1960,43(5):867-896.
[8]REHAN M,HONG K S. Robust synchronization of delayed chaotic FitzHugh-Nagumo neurons under external electrical stimulation[J]. Computational and Mathematical Methods in Medicine,2012:1-11.
[9]FAN D J,HONG L. Hopf bifurcation analysis in a synaptically coupled FHN neuron model with delays[J]. Communications in Nonlinear Science Numerical Simulation,2010,15(7):1873-1886.
[10]ZHANG Y,XIANG L,ZHOU J. Dynamical behaviors in coupled FitzHugh-Nagumo neural systems with time delays[C]//Chinese Intelligent Systems Conference 2016,Singapore,2016:289-300.
[11]YOONSIK S,PHIL H. The chaotic dynamics and multistability of two coupled FitzHugh-Nagumo model neurons[J]. Adaptive Behavior,2018,26(4):165-176.
[12]BAO H B,PARK J H,CAO J D. Exponential synchronization of coupled stochastic memristor-based neural networks with time-varying probabilistic delay coupling and impulsive delay[J]. IEEE Neural Networks and Learning Systems,2016,27(1):190-201.
[13]ZHANG J H,LIAO X F. Synchronization and chaos in coupled memristor-based FitzHugh-Nagumo circuits with memristor synapse[J]. Aeu International Journal of Electronics and Communications,2017,75:82-90.
[14]ZHANG J H,LIAO X F. Effects of initial conditions on the synchronization of the coupled memristor neural circuits[J]. Nonlinear Dynamics,2019,95:1269-1282.
[15]BAO H,LIU W B,CHEN M. Hidden extreme multistability and dimensionality reduction analysis for an improved non-autonomous memristive FitzHugh-Nagumo circuit[J]. Nonlinear Dynamics,2019,96:1879-1894.
[16]CHEN M,QI J W,XU Q,et al. Quasi-period,periodic bursting and bifurcations in memristor-based FitzHugh-Nagumo circuit[J]. AEU-International Journal of Electronics and Communications,2019,110:152840.
[17]WEI W. Synchronization of coupled chaotic Hindmarsh Rose neurons:an adaptive approach[J]. Chinese Physics B,2015,24(10):97-104.
[18]SHE W Q,MA M H. Tracking synchronization of networked lagrangian systems via impulsive control and its applications[J]. Journal of Systems Science and Complexity,2019,32:1093-1103.
[19]OUANNAS A,BENDOUKHA S,VOLOS C,et al. Synchronization of fractional hyperchaotic rabinovich systems via linear and nonlinear control with an application to secure communications[J]. International Journal of Control Automation and Systems,2019,17(9):2211-2219.
[20]AL-SHARGIE F,TANG T B,KIGUCHI M. Stress assessment based on decision fusion of EEG and fNIRS signals[J]. IEEE Access,2017,5:19889-19896.
[21]SUN X J,LI G F. Synchronization transitions induced by partial time delay in a excitatory-inhibitory coupled neuronal network[J]. Nonlinear Dynamics,2017,89(4):2509-2520.
[22]RIGATOS G. Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter[J]. Cognitive Neurodynamics,2014,8(6):465-478.
[23]YU H T,WANG J,DENG B,et al. Adaptive backstepping sliding mode control for chaos synchronization of two coupled neurons in the external electrical stimulation[J]. Communications in Nonlinear Science and Numerical Simulation,2012,17(3):1344-1354.
[24]YANG C C,LIN C L. Robust adaptive sliding mode control for synchronization of space-clamped FitzHugh-Nagumo neurons[J]. Nonlinear Dynamics,2012,69(4):2089-2096.
[25]CHEN S,LIM C,SHI P,et al. Synchronization control for reaction-diffusion FitzHugh-Nagumo systems with spatial sampled-data[J]. Automatica,2018,93:352-362.
[26]IBRAHIM M M,JUNG H. Complex synchronization of a ring-structured network of FitzHugh-Nagumo neurons with single- and dual-state gap junctions under ionic gates and external electrical disturbance[J]. IEEE Access,2019,7:57894-57906.
[27]JóZSEF K T,JáNOS F B,LEVENTE K,et al. Fractional order PID-type feedback in fixed point transformation-based adaptive control of the FitzHugh-Nagumo neuron model with time-delay[J]. IFAC-Papersonline,2018,51(4):906-911.
[28]PLOTNIKOV S A,LEHNERT J,FRADKOV A L,et al. Adaptive control of synchronization in delay-coupled heterogeneous networks of FitzHugh-Nagumo nodes[J]. International Journal of Bifurcation and Chaos,2016,26(4):165008.
[29]CHEN M,FENG Y,BAO H,et al. State variable mapping method for studying initial-dependent dynamics in memristive hyper-jerk system with line equilibrium[J]. Chaos’ Solitons and Fractals,2018,115:313-324.
[30]BAO H,CAO J. Finite-time generalized synchronization of nonidentical delayed chaotic systems[J]. Nonlinear Analysis:Modeling and Control,2016,21(3):306-324.

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备注/Memo

备注/Memo:
收稿日期:2019-11-29.
基金项目:国家自然科学基金项目(61971228).
通讯作者:闵富红,博士,教授,研究方向:非线性电路系统. E-mail:minfuhong@njnu.edu.cn
更新日期/Last Update: 2020-05-15