Waveforms classification based on convolutional neural networks

摘要：

　　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.

关键词： waveforms classification convolution pooling convolutional neural networks

作者: Bendong Zhao Shanzhu Xiao Huanzhang Lu Junliang Liu

作者单位: National University of Defense Technology Changsha,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 162-165

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）