基于改进的忆阻器在字符联想记忆中的应用

王雷敏; 程佳俊; 胡成; 周映江; 葛明峰

doi:10.11999/JEIT220709

基于改进的忆阻器在字符联想记忆中的应用

doi: 10.11999/JEIT220709

王雷敏^{1, 2, 3, ,},
程佳俊^{1, 2, 3},
胡成⁴,
周映江⁵,
葛明峰⁶

1.
中国地质大学(武汉)自动化学院武汉 430074
2.
复杂系统先进控制与智能自动化湖北省重点实验室武汉 430074
3.
地球探测智能化技术教育部工程研究中心武汉 430074
4.
新疆大学数学与系统科学学院乌鲁木齐 830017
5.
南京邮电大学自动化学院南京 210003
6.
中国地质大学(武汉)机械与电子信息学院武汉 430074

基金项目: 国家自然科学基金(62076229, 61963033, 62073301)

详细信息

作者简介:
王雷敏：男，教授，研究方向为忆阻电路设计及忆阻神经网络应用

程佳俊：男，硕士生，研究方向为忆阻电路设计及忆阻神经网络应用

胡成：男，教授，研究方向为神经网络动力学及应用

周映江：男，副教授，研究方向为多智能体系统分析及应用

葛明峰：男，教授，研究方向为多机器人系统分析及应用

通讯作者:
王雷敏　wangleimin@cug.edu.cn

中图分类号: TN601
计量
- 文章访问数: 381
- HTML全文浏览量: 150
- PDF下载量: 91
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-31
- 修回日期: 2022-09-15
- 网络出版日期: 2022-11-20
- 刊出日期: 2023-07-10

Application of Improved Memristor in Character Associative Memory

WANG Leimin^{1, 2, 3
, ,},
CHENG Jiajun^{1, 2, 3},
HU Cheng⁴,
ZHOU Yingjiang⁵,
GE Mingfeng⁶

1.
School of Automation, China University of Geosciences, Wuhan 430074, China
2.
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3.
Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
4.
School of Mathematics and Systems Science, Xinjiang University, Urumqi 830017, China
5.
School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
6.
School of Mechanical and Electronic Information, China University of Geosciences, Wuhan 430074, China

Funds: The National Natural Science Foundation of China (62076229, 61963033, 62073301)

摘要

摘要: 忆阻因具有阻值可调、记忆特性以及纳米尺寸等特点，非常适合作为实现神经网络突触的电子元器件。为构建出更加符合真实物理忆阻器特性的忆阻器模型，该文在现有忆阻器模型的基础之上，克服了边界锁定、正负电压调整速率问题以及电路结构通用性问题，提出一种改进忆阻器模型。然后结合Pavlov联想记忆实验和Hopfield神经网络理论设计出了该文的字符联想记忆电路。电路结构主要有输入信号模块、突触阵列模块、激活函数模块以及反馈控制模块。该电路可以解决因传统阵列模块使用电阻作为突触模块的灵活性问题，而且还可以实现对3阶字符模糊图像的自联想功能。此外，该电路与深度学习相关的卷积计算模块原理类似，为实现基于忆阻的智能硬件奠定了理论基础。
- 忆阻器 /
- 神经网络 /
- 电路设计 /
- 联想记忆
Abstract: Memristor is a very suitable electronic component for synapse of neural network because of its adjustable resistance, memory property and nano size. In order to build a memristor model more consistent with the characteristics of real physical memristors, an improved memristor model is proposed based on existing ones to overcome the problems of boundary locking, positive and negative voltage rate adjustment and the universality of circuit structure. Then combining Pavlov associative memory experiment and Hopfield neural network theory, the character associative memory circuit is designed in this paper. The circuit structure includes mainly input signal module, synaptic array module, activation function module and feedback control module. This circuit can solve the flexibility problem of using resistors as synaptic modules in traditional array modules, and can also realize the self-association function of third-order character blurred images. In addition, the circuit is similar to the convolutional computation module related to deep learning, and provides a theoretical basis for realizing memristor-based intelligent hardware.
- Memristor /
- Neural network /
- Circuit design /
- Associative memory

HTML全文

图 1 DHNNs结构示意图

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图 2 忆阻神经网络电路结构

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图 3 忆阻神经网络整体电路

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图 4 本文神经网络模型

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图 5 忆阻神经网络联想记忆过程

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图 6 字符“C”神经元输出电压曲线图

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图 7 字符“U”神经元输出电压曲线图

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图 8 字符“H”神经元输出电压曲线图

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表 1 本文模型与现有的部分代表性忆阻模型的比较

文献	功能实现
文献	伏安特性	边界锁定	控制类型	阈值特性	遗忘特性	逻辑电路	突触电路
线性漂移^[2]	明确	存在问题	电流	无	无	不适用	不适用
Joglekar^[25]	明确	存在问题	电流	无	无	不适用	不适用
Biolek^[26]	明确	无	电流	无	无	适用	不适用
TEAM^[27]	明确	无	电流	有	无	适用	不适用
VTEAM^[28]	明确	无	电压	有	无	适用	不适用
本文	明确	无	电压	有	有	适用	适用

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表 2 忆阻联想记忆模型功能完善程度对比

文献	功能实现
文献	联想记忆	可重用	抗噪声	连续性
[10, 17]	√
[18, 20]	√		√	√
本文	√	√	√	√

下载: 导出CSV

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