计算机应用 ›› 2020, Vol. 40 ›› Issue (3): 698-703.DOI: 10.11772/j.issn.1001-9081.2019081435

• 人工智能 • 上一篇    下一篇

降噪自编码器深度卷积过程神经网络及在时变信号分类中的应用

朱喆, 许少华   

  1. 山东科技大学 计算机科学与工程学院, 山东 青岛 266590
  • 收稿日期:2019-08-16 修回日期:2019-10-17 出版日期:2020-03-10 发布日期:2019-11-20
  • 通讯作者: 朱喆
  • 作者简介:朱喆(1991-),男,山东泰安人,硕士研究生,CCF会员,主要研究方向:深度学习、大数据分析;许少华(1962-),男,河北邢台人,教授,博士,主要研究方向:人工智能、大数据建模分析。

Denoising autoencoder deep convolution process neural network and its application in time-varying signal classification

ZHU Zhe, XU Shaohua   

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao Shandong 266590, China
  • Received:2019-08-16 Revised:2019-10-17 Online:2020-03-10 Published:2019-11-20

摘要: 针对非线性时变信号分类问题,将过程神经网络(PNN)的信息处理机制与卷积运算相结合,提出了一种降噪自编码器深度卷积过程神经网络(DAE-DCPNN)。该模型由时变信号输入层、卷积过程神经元(CPN)隐层、深度降噪自动编码器(DAE)网络结构和softmax分类器构成。CPN的输入为时序信号,卷积核取为具有梯度性质的5阶数组,基于滑动窗口进行卷积运算,实现时序信号的时空聚合和过程特征提取。在CPN隐层之后,栈式叠加DAE深度网络和softmax分类器,实现对时变信号特征高层次的提取和分类。分析了DAE-DCPNN的性质,给出了按各信息单元分别进行赋初值训练、模型参数整体调优的综合训练算法。以基于12导联心电图(ECG)信号对7种心血管疾病分类诊断为例,实验结果验证了所提模型和算法的有效性。

关键词: 时变信号分类, 卷积过程神经元, 降噪自编码器, 卷积过程神经网络, 特征提取, 心电图信号分类

Abstract: To solve the problem of nonlinear time-varying signal classification, a Denoising AutoEncoder Deep Convolution Process Neural Network (DAE-DCPNN) was proposed, which combines the information processing mechanism of Process Neural Network (PNN) with convolution operation. The model consists of a time-varying signal input layer, a Convolution Process Neuron (CPN) hidden layer, a deep Denoising AutoEncoder (DAE) network structure and a softmax classifier. The inputs of CPN were time-series signals, and the convolution kernel was taken as a five-order array with gradient property. And convolution operation was carried out based on sliding window to realize the spatio-temporal aggregation of time-series signals and the extraction of process features. After the CPN hidden layer, the DAE deep network and the softmax classifier were stacked to realize the high-level extraction and classification of features of time-varying signals. The properties of DAE-DCPNN were analyzed, and the comprehensive training algorithm of the initial value assignment training based on each information unit and the overall optimization of model parameters was given. Taking 7 kinds of cardiovascular disease classification diagnosis based on 12-lead ElectroCardioGram (ECG) signals as an example, the experimental results verify the effectiveness of the proposed model and algorithm.

Key words: time-varying signal classification, Convolution Process Neuron (CPN), Denoising AutoEncoder (DAE), Convolution Process Neural Network (CPNN), feature extraction, ElectroCardioGram (ECG) signal classification

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