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计算机工程 ›› 2008, Vol. 34 ›› Issue (6): 25-27. doi: 10.3969/j.issn.1000-3428.2008.06.008

• 博士论文 • 上一篇    下一篇

浅海混响时间序列的支持向量机预测

高 伟,王 宁   

  1. (中国海洋大学物理系,青岛 266100)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-03-20 发布日期:2008-03-20

Prediction of Shallow-water Reverberation Time Series Using Support Vector Machine

GAO Wei, WANG Ning   

  1. (Department of Physics, Ocean University of China, Qingdao 266100)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-03-20 Published:2008-03-20

摘要: 把基于结构风险最小化原则的支持向量机应用到混响时间序列预测中,与径向基函数(RBF)神经网络方法预测结果进行了对比分析。采用海上实验混响数据进行预测,处理结果表明,支持向量机的方法优于RBF神经网络的方法,对混响时间序列有很好的预测效果。

关键词: 浅海混响, 支持向量机, 径向基函数神经网络, 预测

Abstract: This paper applies Support Vector Machine (SVM) based on the rule of structural risk minimization to the reverberation time series prediction, and compares the predict results with Radial Basic Function(RBF) neural network. Experimental results of an ocean reverberation show that the SVM method is more effective than RBF and suitable to predict the reverberation time series.

Key words: shallow-water reverberation, Support Vector Machine(SVM), Radial Basic Function(RBF) neural network, prediction

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