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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(x): 1−15 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(x): 1−15 doi: 10.16383/j.aas.c200691
Zhang Lian-Sheng, Jin Yao-Chu, Song Yong-Rui. An overview of dynamics analysis and control of memristive neural networks with delays. Acta Automatica Sinica, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c200691
Citation: Zhang Lian-Sheng, Jin Yao-Chu, Song Yong-Rui. An overview of dynamics analysis and control of memristive neural networks with delays. Acta Automatica Sinica, 2021, 47(x): 1−15 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:博士, 北京石油化工学院教师. 主要研究方向是鲁棒控制、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

    英国萨里大学计算科学系“计算智能”讲席教授“自然计算与应用”研究组主任. 金耀初博士是教育部“长江学者奖励计划”讲座教授, 芬兰国家技术创新局“芬兰讲座教授”, 澳大利亚悉尼技术大学“杰出访问学者”. 主要研究领域为数据驱动的进化优化、可信机器学习、多目标学习、群机器人及演化发育系统. E-mail: lcjx@mail.neu.edu.cn

    宋永端:中国自动化学会常务理事, IEEE会士, 中国自动化学会“可信控制系统”专委会主任, IEEE CIS重庆计算智能分会主席, 担任包括IEEE Translation on Automatic Control在内的6部国际学术杂志编委. 1992 年获得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, 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 P. R. China (61773081, 61833013), Science and Technology Plan of Beijing Municipal Education Commission (KM201910017002)
  • 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了一种全新的架构. 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, 因此考虑含各种时滞, 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. 本文首先介绍了忆阻器的多种数学模型及其分类, 建立了忆阻神经网络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、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, 对本文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[17]

    Fig.  2  Circuit of memristor-based recurrent network[17]

    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$$i = W(\varphi )v$
    $\dfrac{ {dq} }{ {dt} } = i$$\dfrac{ {d\varphi } }{ {dt} } = v$
    理想通用型忆阻器$v = M(x)i$$i = W(x)v$
    $\dfrac{ {dx} }{ {dt} } = f(x)i$$\dfrac{ {dx} }{ {dt} } = g(x)v$
    通用型忆阻器$v = M(x)i$$i = W(x)v$
    $\dfrac{ {dx} }{ {dt} } = f(x,i)$$\dfrac{ {dx} }{ {dt} } = g(x,v)$
    拓展型忆阻器$\begin{align} v = M(x,i)i \\ M(x,0) \ne \infty \end{align}$$\begin{align} i = W(x,v)v \\ W(x,v) \ne 0 \end{align}$
    $\dfrac{ {dx} }{ {dt} } = f(x,i)$$\dfrac{ {dx} }{ {dt} } = g(x,v)$
    下载: 导出CSV
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  • 收稿日期:  2020-08-28
  • 录用日期:  2020-12-14
  • 网络出版日期:  2021-01-11

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