高等学校化学学报

高等学校化学学报 ›› 2000, Vol. 21 ›› Issue (6): 855.

• 论文 • 上一篇    下一篇

基于小波神经网络的新型算法用于化学信号处理

蔡文生1, 于芳1, 邵学广2, 潘忠孝1   

  1. 1. 中国科技大学应用化学系, 合肥 230026;
    2. 中国科技大学化学系, 合肥 230026
  • 收稿日期:1999-09-20 出版日期:2000-06-24 发布日期:2000-06-24
  • 通讯作者: 蔡文生(1965年出生),女,博士,副教授,主要从事化学计量学研究.
  • 基金资助:

    国家自然科学基金(批准号:29775001)

A Novel Algorithm Based on the Wavelet Neural Network for Processing Chemical Signals

CAI Wen-Sheng1, YU Fang1, SHAO Xue-Guang2, PAN Zhong-Xiao1   

  1. 1. Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, China;
    2. Department of Chemistry, University of Science and Technology of China, Hefei 230026, China
  • Received:1999-09-20 Online:2000-06-24 Published:2000-06-24

PDF (PC)

被引次数

67

摘要： 基于紧支集正交小波神经网络的构造思想,用具有紧支集的B-样条函数的伸缩和平移替代小波函数,提出了一种新型算法,并将其应用于化学信号的处理,实现了信号的压缩和滤噪,与自适应小波神经网络相比,学习速度得到了大幅度的提高.

关键词: 小波神经网络, B-样条函数, 压缩, 滤噪

Abstract: Awavelet neural network based on wavelet analysis can be used to represent chemical signals. The adaptive wavelet neural network using the continuous wavelet transform has problems of a high redundancy and slow training, and the compactly supported orthogonal wavelet neural network using the discrete wavelet transform is difficult to be applied, because the compactly supported orthogonal wavelet function with analytic form is hard to build. Based on the idea of the compactly supported orthogonal wavelet network, a novel algorithm using the compactly supported B-spline function instead of the compactly supported or thogonal wavelet function is proposed. It has been applied to the compression and de- noising of chemical signals. Compared with the adaptive wavelet neural network, the speed of our algorithm was enhanced greatly.

Key words: Wavelet neural network, B-spline function, Compression, De-noising

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