Waveforms classification based on convolutional neural networks

A novel waveforms classification method based on convolutional neural networks (CNN) is proposed in this paper. Firstly, convolution and pooling operations are cross used for generating deep features, and then fully connected to the output layer for classification. Different from other traditional a...

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Bibliographic Details
Published in2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) pp. 162 - 165
Main Authors Bendong Zhao, Shanzhu Xiao, Huanzhang Lu, Junliang Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2017
Subjects
Biological neural networks
Convolution
convolutional neural networks
Feature extraction
Neurons
pooling
Signal to noise ratio
Support vector machines
Training
waveforms classification
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Summary:A novel waveforms classification method based on convolutional neural networks (CNN) is proposed in this paper. Firstly, convolution and pooling operations are cross used for generating deep features, and then fully connected to the output layer for classification. Different from other traditional approaches which need human-designed features, CNN can discover and extract the suitable internal structure of the input waveform to obtain deep features for classification automatically. So that the generalization ability of this method is significantly improved comparing to other methods. Experimental results show that CNN can obtain state of the art performance for waveforms classification in terms of classification accuracy and noise tolerance.
DOI:10.1109/IAEAC.2017.8053998