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时滞忆阻神经网络动力学分析与控制综述

章联生 金耀初 宋永端

章联生, 金耀初, 宋永端. 时滞忆阻神经网络动力学分析与控制综述. 自动化学报, 2021, 47(4): 765−779 doi: 10.16383/j.aas.c200691
引用本文: 章联生, 金耀初, 宋永端. 时滞忆阻神经网络动力学分析与控制综述. 自动化学报, 2021, 47(4): 765−779 doi: 10.16383/j.aas.c200691
Zhang Lian-Sheng, Jin Yao-Chu, Song Yong-Duan. An overview of dynamics analysis and control of memristive neural networks with delays. Acta Automatica Sinica, 2021, 47(4): 765−779 doi: 10.16383/j.aas.c200691
Citation: Zhang Lian-Sheng, Jin Yao-Chu, Song Yong-Duan. An overview of dynamics analysis and control of memristive neural networks with delays. Acta Automatica Sinica, 2021, 47(4): 765−779 doi: 10.16383/j.aas.c200691

时滞忆阻神经网络动力学分析与控制综述

doi: 10.16383/j.aas.c200691
基金项目: 国家自然科学基金(61773081, 61833013), 北京市教委科技计划一般项目(KM201910017002)资助
详细信息
    作者简介:

    章联生:北京石油化工学院副教授. 主要研究方向为鲁棒控制, 自适应控制, 时滞系统、随机系统、神经网络及其应用. E-mail: zhangliansheng@bipt.edu.cn

    金耀初:英国萨里大学计算科学系教授. 主要研究方向为数据驱动的进化优化, 可信机器学习, 多目标学习, 群机器人和演化发育系统. E-mail: lcjx@mail.neu.edu.cn

    宋永端:重庆大学自动化学院院长, 重庆大学人工智能研究院院长, 教授. IEEE Fellow, 国际欧亚科学院院士. 1992年获美国田纳西理工大学电气与计算机博士学位. 主要研究方向为智慧系统, 导航与控制, 仿生自适应控制和系统安全与控制. 本文通信作者. E-mail: ydsong@cqu.edu.cn

An Overview of Dynamics Analysis and Control of Memristive Neural Networks With Delays

Funds: Supported by National Natural Science Foundation of China (61773081, 61833013), Science and Technology Plan of Beijing Municipal Education Commission (KM201910017002)
More Information
    Author Bio:

    ZHANG Lian-Sheng Associate professor of Beijing Institute of Petro-chemical Technology. His research interest covers robust control, adaptive control, and time-delayed system, stochastic systems, neural networks and their applications

    JIN Yao-Chu Professor in the Department of Computer Science, University of Surrey, Guildford, UK. His research interest covers data-driven surrogateassisted evolutionary optimization, trustworthy machine learning, multi-objective evolutionary learning, swarm robotics, and evolutionary developmental systems

    SONG Yong-Duan Dean of School of Automation, Dean of Institute for Artifical Intelligence, Chongqing University, China. Fellow of IEEE, Fellow of International Eurasian Academy of Sciences. He received his Ph. D. degree in electrical and computer engineering from Tennessee Technological University, Cookeville, TN, USA in 1992. His research interest covers intelligent systems, guidance navigation and control, bioinspired adaptive control, and system cooperation and reliability. Corresponding author of this paper

  • 摘要: 忆阻器(Memristor)是一种无源的二端电子元件, 同时也是一种纳米级元件, 具有低能耗、高存储、小体积和非易失性等特点. 作为一种新型的存储器件, 忆阻器的研制, 有望使计算机实现人脑特有的信息存储与信息处理一体化的功能, 打破目前冯·诺伊曼(Von Neumann)计算机架构, 为下一代计算机的研制提供一种全新的架构. 鉴于忆阻器与生物神经元突触具有十分相似的功能, 使忆阻器得以充当人工神经元的突触, 建立起一种基于忆阻器的人工神经网络即忆阻神经网络. 忆阻器的问世, 为人工神经网络从电路上模拟人脑提供了可能, 必将极大推动人工智能的发展. 此外, 忆阻神经网络的硬件实现及信号传递过程中, 不可避免会出现时滞与分岔等现象, 因此讨论含各种时滞, 如离散、分布、泄漏时滞以及它们混合的时滞忆阻神经网络系统更具有现实意义. 首先介绍了忆阻器的多种数学模型及其分类, 建立了时滞忆阻神经网络(Delayed memristive neural networks, DMNN)的数学模型并阐述了其优点. 然后提出了处理时滞忆阻神经网络动力学行为与控制问题的两种思路, 详细综述了时滞忆阻神经网络系统的稳定性(镇定)、耗散性与无源性及其同步控制方面的内容, 简述了其他方面的动力学行为与控制, 并介绍了时滞忆阻神经网络动力学行为与控制研究新方向. 最后, 对所述问题进行了总结与展望.
  • 图  1  四个基本二端电路元件关系图

    Fig.  1  The four fundamental two-terminal circuit elements

    图  2  基于忆阻器的递归神经网络电路图[9]

    Fig.  2  Circuit of memristor-based recurrent network[9]

    图  3  忆阻器的电流—电压特性曲线

    Fig.  3  Typical current-voltage characteristics of a memristor

    表  1  四类忆阻器

    Table  1  Four classes of memristors

    种类电流控制型电压控制型
    理想型忆阻器$v = M(q)i$$i = W(\varphi )v$
    $\dfrac{ {{\rm{d}}q} }{ {{\rm{d}}t} } = i$$\dfrac{ {{\rm{d}}\varphi } }{ {{\rm{d}}t} } = v$
    理想通用型忆阻器$v = M(x)i$$i = W(x)v$
    $\dfrac{ {{\rm{d}}x} }{ {{\rm{d}}t} } = f(x)i$$\dfrac{ {{\rm{d}}x} }{ {{\rm{d}}t} } = g(x)v$
    通用型忆阻器$v = M(x)i$$i = W(x)v$
    $\dfrac{ {{\rm{d}}x} }{ {{\rm{d}}t} } = f(x,i)$$\dfrac{ {{\rm{d}}x} }{ {{\rm{d}}t} } = g(x,v)$
    拓展型忆阻器$\begin{align} v = M(x,i)i \\ M(x,0) \ne \infty \end{align}$$\begin{align} i = W(x,v)v \\ W(x,v) \ne 0 \end{align}$
    $\dfrac{ {{\rm{d}}x} }{ {{\rm{d}}t} } = f(x,i)$$\dfrac{ {{\rm{d}}x} }{ {{\rm{d}}t} } = g(x,v)$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-28
  • 录用日期:  2020-12-14
  • 网络出版日期:  2021-01-11
  • 刊出日期:  2021-04-23

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