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

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。

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

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.