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量子门Elman神经网络及其梯度扩展的量子反向传播学习算法

李鹏华 柴毅 熊庆宇

李鹏华, 柴毅, 熊庆宇. 量子门Elman神经网络及其梯度扩展的量子反向传播学习算法. 自动化学报, 2013, 39(9): 1511-1522. doi: 10.3724/SP.J.1004.2013.01511
引用本文: 李鹏华, 柴毅, 熊庆宇. 量子门Elman神经网络及其梯度扩展的量子反向传播学习算法. 自动化学报, 2013, 39(9): 1511-1522. doi: 10.3724/SP.J.1004.2013.01511
LI Peng-Hua, CHAI Yi, XIONG Qing-Yu. Quantum Gate Elman Neural Network and Its Quantized Extended Gradient Back-propagation Training Algorithm. ACTA AUTOMATICA SINICA, 2013, 39(9): 1511-1522. doi: 10.3724/SP.J.1004.2013.01511
Citation: LI Peng-Hua, CHAI Yi, XIONG Qing-Yu. Quantum Gate Elman Neural Network and Its Quantized Extended Gradient Back-propagation Training Algorithm. ACTA AUTOMATICA SINICA, 2013, 39(9): 1511-1522. doi: 10.3724/SP.J.1004.2013.01511

量子门Elman神经网络及其梯度扩展的量子反向传播学习算法

doi: 10.3724/SP.J.1004.2013.01511
基金项目: 

国家自然科学基金(60974090);高等学校博士学科点专项科研基金(20100191110037)资助

详细信息
    作者简介:

    李鹏华 重庆大学自动化学院博士研究生.2008年获得重庆大学理学学士学位. 主要研究方向为人工神经网络,量子神经计算及其应用.E-mail: lipenghua88@163.com

Quantum Gate Elman Neural Network and Its Quantized Extended Gradient Back-propagation Training Algorithm

Funds: 

Supported by National Natural Science Foundation of China (60974090), Research Fund for the Doctoral Program of Higher Education of China (20100191110037)

  • 摘要: 针对Elman神经网络的学习速度和泛化性能, 提出一种具有量子门结构的新型Elman神经网络模型及其梯度扩展反向传播(Back-propagation)学习算法, 新模型由量子比特神经元和经典神经元构成. 新网络结构采用量子映射层以确保来自上下文单元的局部反馈与隐藏层输入之间的模式一致; 通过量子比特神经元输出与相关量子门参数的修正互补关系以提高网络更新动力. 新学习算法采用搜索然后收敛的策略自适应地调整学习率参数以提高网络学习速度; 通过将上下文单元的权值扩展到隐藏层的权值矩阵, 使其在与隐藏层权值同步更新过程中获取时间序列的额外信息, 从而提高网络上下文单元输出与隐藏层输入之间的匹配程度. 以峰值检波为例的数值实验结果显示, 在量子反向传播学习过程中, 量子门Elman神经网络具有较快的学习速度和良好的泛化性能.
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出版历程
  • 收稿日期:  2012-04-16
  • 修回日期:  2012-09-22
  • 刊出日期:  2013-09-20

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