基于扩维的卷积网络及脉象识别应用

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 506-507.

基于扩维的卷积网络及脉象识别应用

张宁

中央财经大学中国精算研究院北京100081

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:张宁(1978-),男,博士,副教授,主要研究方向为金融科技、健康量化、精算与长寿风险,E-mail:[email protected](通信作者)。
基金资助:
教育部人文社科项目(16YJCZH148),重点研究基地重大项目(16JJD790060),中国保险学会教保人身险研究基金(jiaobao2017-10),高校学科创新引智计划资助(B17050),厦门产学研协同创新及科技合作项目(3502Z20172012),中央财经大学科研创新团队资助(20170074)资助。

Pulse Condition Recognition Based on Convolutional Neural Network with Dimension Enlarging

ZHANG Ning

China Institute for Actuarial Science,Central University of Finance and Economics,Beijing 100081,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 针对时间尺度变化特征差异较大的非图像多元时间序列,提出了一种卷积神经网络的扩维预处理方法。该方法应用样本统计特征和希尔伯特-黄变换来扩展维度,并加快网络的训练。文中将其用于生理数据分析并进行脉象分类。结果表明,进行扩维能够较大幅度地改善随机梯度算法的效率,同时该卷积网络方法能够较好地捕捉生理信号和脉象的特征关系。

关键词: 经验模型分解, 卷积神经网络, 脉象, 统计特征, 希尔伯特-黄变换

Abstract: A new model of convolutional neural network was promoted for pulse condition recognition.The model is fit for the group including different dimensional data sets.For more effective training process,the sample characters and HHT’s results were considered as a times series.The result shows the expected accuracy rating and training efficiency.The method can also obtain the relations between pulse conditions and several personal biological data.

Key words: Convolutional neural network, Empirical mode decomposition, Hilbert-Huang transform, Pulse condition, Statistical characters

中图分类号:

TP301

张宁. 基于扩维的卷积网络及脉象识别应用[J]. 计算机科学, 2018, 45(6A): 506-507. https://doi.org/

ZHANG Ning. Pulse Condition Recognition Based on Convolutional Neural Network with Dimension Enlarging[J]. Computer Science, 2018, 45(6A): 506-507. https://doi.org/

参考文献

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[3]ALTWAIJRY H,TRULLS E,HAYS J,et al.Learning to match aerial images with deep attentive architectures[C]∥Computer Vision and Pattern Recognition.IEEE,2016:3539-3547.
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