A Sparse Auto Encoder Deep Process Neural Network Model and its Application

DOI

10.2991/ijcis.2017.10.1.74How to use a DOI?

Keywords

time-varying signal classification; process neural network; deep learning; SAE; training algorithm

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Xu Shaohua
AU  - Xue Jiwei
AU  - Li Xuegui
PY  - 2017
DA  - 2017/07/28
TI  - A Sparse Auto Encoder Deep Process Neural Network Model and its Application
JO  - International Journal of Computational Intelligence Systems
SP  - 1116
EP  - 1131
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.74
DO  - 10.2991/ijcis.2017.10.1.74
ID  - Shaohua2017
ER  -