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  

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

作　　者：朱喆 许少华 ZHU Zhe;XU Shaohua(College of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao Shandong 266590,China)

机构地区：[1]山东科技大学计算机科学与工程学院

出　　处：《计算机应用》2020年第3期698-703,共6页journal of Computer Applications

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。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.

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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]