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深度信念网络研究现状与展望

王功明 乔俊飞 关丽娜 贾庆山

王功明, 乔俊飞, 关丽娜, 贾庆山.深度信念网络研究现状与展望.自动化学报, 2021, 47(1): 35-49 doi: 10.16383/j.aas.c190102
引用本文: 王功明, 乔俊飞, 关丽娜, 贾庆山.深度信念网络研究现状与展望.自动化学报, 2021, 47(1): 35-49 doi: 10.16383/j.aas.c190102
Wang Gong-Ming, Qiao Jun-Fei, Guan Li-Na, Jia Qing-Shan. Review and prospect on deep belief network.Acta Automatica Sinica, 2021, 47(1): 35-49 doi: 10.16383/j.aas.c190102
Citation: Wang Gong-Ming, Qiao Jun-Fei, Guan Li-Na, Jia Qing-Shan. Review and prospect on deep belief network. Acta Automatica Sinica, 2021, 47(1): 35-49 doi: 10.16383/j.aas.c190102

深度信念网络研究现状与展望

doi: 10.16383/j.aas.c190102
基金项目: 

国家自然科学基金 61533002

详细信息
    作者简介:

    乔俊飞  北京工业大学信息学部自动化学院教授.主要研究方向为污水处理过程智能控制, 神经网络结构设计与分析. E-mail: junfeq@bjut.edu.cn

    关丽娜  北京工业大学信息学部博士研究生.主要研究方向为双曲系统稳定性分析及鲁棒控制. E-mail:guanlina@emails.bjut.edu.cn

    贾庆山  清华大学自动化系智能与网络化系统研究中心副教授.主要研究方向为大规模复杂系统的优化控制理论与方法研究, 并将其应用于能源系统、制造系统、建筑系统、疏散控制系统、机器人系统、生物系统、信息物理系统以及物联网系统等. E-mail: jiaqs@tsinghua.edu.cn

    通讯作者:

    王功明  北京工业大学信息学部博士研究生.主要研究方向为深度学习, 神经网络结构设计与优化控制策略.本文通信作者. E-mail: xiaowangqsd@163.com

  • 本文责任编委 张敏灵

Review and Prospect on Deep Belief Network

Funds: 

National Natural Science Foundation of China 61533002

More Information
    Author Bio:

    QIAO Jun-Fei   Professor at Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent control of wastewater treatment process, structure design and analysis for neural networks

    GUAN Li-Na  Ph. D. candidate at Faculty of Information Technology, Beijing University of Technology. Her research interest covers stability analysis and robust control for hyperbolic system

    JIA Qing-Shan   Associate professor at Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University. His research interest covers optimization control theory and method research for large-scale complex systems and their applications in the energy system, manufacturing systems, building systems, evacuation control systems, robot systems, biological systems, cyber-physical systems and internet of things systems

    Corresponding author: WANG Gong-Ming   Ph. D. candidate at Faculty of Information Technology, Beijing University of Technology. His research interest covers deep learning, structure design and optimization control strategy for neural networks. Corresponding author of this paper
  • Recommended by Associate Editor ZHANG Min-Ling
  • 摘要: 深度信念网络(Deep belief network, DBN)是一种基于深度学习的生成模型, 克服了传统梯度类学习算法在处理深层结构所面临的梯度消失问题, 近几年来已成为深度学习领域的研究热点之一.基于分阶段学习的思想, 人们设计了不同结构和学习算法的深度信念网络模型.本文在回顾总结深度信念网络的研究现状基础上, 给出了其发展趋势.首先, 给出深度信念网络的基本模型结构以及其标准的学习框架, 并分析了深度信念网络与其他深度结构的关系与区别; 其次, 回顾总结深度信念网络研究现状, 基于标准模型分析不同深度信念网络结构的性能; 第三, 给出深度信念网络的不同无监督预训练和有监督调优算法, 并分析其性能; 最后, 给出深度信念网络今后的发展趋势以及未来值得研究的方向.
    Recommended by Associate Editor ZHANG Min-Ling
    1)  本文责任编委 张敏灵
  • 图  1  RBM结构图

    Fig.  1  Structure of RBM

    图  2  DBN结构图

    Fig.  2  Structure of DBN

    图  3  稀疏表述原理图

    Fig.  3  Sparse representation scheme

    图  4  计算激活强度的权值连接过程

    Fig.  4  Weights connecting process of computing spiking intensity

    图  5  结构自组织策略原理图

    Fig.  5  Self-organizing structure strategy scheme

    图  6  TL-GDBN的一步增长过程

    Fig.  6  Illustration of one-growing step

    图  7  RTRBM的结构图

    Fig.  7  RTRBM structure

    图  8  RNN-RBM的结构图

    Fig.  8  RNN-RBM structure

    图  9  RNN-DBN的结构图

    Fig.  9  RNN-DBN structure

    图  10  半监督RBM结构

    Fig.  10  Structure of semi-supervised RBM

    图  11  基于PLSR的DBN调优

    Fig.  11  PLSR-based fine-tuning of DBN

    表  1  不同DBN结构的性能对比

    Table  1  Performance comparison of different DBN structures

    结构 训练RMSE 训练时间(s) 测试时间(s)
    均值 方差
    稀疏DBN 0.0468 0.0009 6.91 5.13
    自组织DBN 0.0308 0.0085 6.50 5.06
    增量式DBN 0.0173 0.0012 4.27 3.14
    递归DBN 0.0149 0.0126 6.67 5.11
    下载: 导出CSV

    表  2  不同DBN算法的性能对比

    Table  2  Performance comparison of different DBN algorithms

    算法 训练RMSE 训练时间(s) 测试时间(s)
    均值 方差
    梯度下降 0.0835 0.0116 12.38 10.09
    自适应学习率 0.0225 0.0102 2.97 1.39
    半监督学习 0.0507 0.0130 8.68 6.17
    偏最小二乘回归 0.0193 0.0091 3.62 2.28
    下载: 导出CSV

    附表 1  文中用到的主要数学符号

    附表 1  Main mathematical notations in this paper

    主要数学符号说明
    ${\mathit{\boldsymbol{v}}}$——可视层神经元组成的状态向量
    ${\mathit{\boldsymbol{h}}}$——隐含层神经元组成的状态向量
    ${\mathit{\boldsymbol{b}}}_v$——可视层神经元偏置状态向量
    ${\mathit{\boldsymbol{b}}}_h$——隐含层神经元偏置状态向量
    ${\mathit{\boldsymbol{c}}}_u$——监督层神经元偏置状态向量
    ${\mathit{\boldsymbol{w}}}^R$——标准受限玻尔兹曼机权值矩阵
    ${\mathit{\boldsymbol{p}}}$——监督层与隐含层之间的权值矩阵
    ${\mathit{\boldsymbol{w}}}_{\rm out}$——最后一个隐含层与输出层之间的权值矩阵
    ${\mathit{\boldsymbol{W}}}^R$——整个网络的初始化权值矩阵
    ${\mathit{\boldsymbol{W}}}$——整个网络的最终权值矩阵
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-02-25
  • 录用日期:  2019-05-19
  • 刊出日期:  2021-01-29

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