A Sparse Auto Encoder Deep Process Neural Network Model and its Application
Shaohua Xu, Xue Ji-wei, Xuegui Li · International Journal of Computational Intelligence Systems · 2017
Aiming at the problem of time-varying signal pattern classification, a sparse auto-encoder deep process neural network (SAE-DPNN) is proposed.The input of SAE-DPNN is time-varying process signal and the output is pattern category.It combines the time-varying signal classification method of process neural network (PNN) and the data feature extraction and hierarchical sparse representation mechanism of sparse automatic encoder (SAE).Based on the feedforward PNN model, SAE-DPNN is constructed by stacking the process neurons, SAE network and softmax classifier.It can maintain the time-sequence and structure of the input signal, express and synthesize the process distribution characteristics of multidimensional time-varying signals and their combinations.SAE-DPNN improves the identification of complex features and distinguishes between different types of signals, realizes the direct classification of time-varying signals.In this paper, the feature extraction and representation mechanism of time-varying signal in SAE-DPNN are analyzed, and a specific learning algorithm is given.The experimental results verify the effectiveness of the model and algorithm.