SVM IN THE SAND-DUST STORM FORECASTING

摘要：

A novel method of the support vector machine (SVM) is proposed in the sand-dust storm-forecasting model. The development of the model includes pre-treating original data by using principal component analysis (PCA), choosing a kernel function (i.e. the Radial Basic Function (RBF) kernel), defining the search region of (C, σ2 ) by analyzing the influence on SVM classifier of the regularization parameter and the kernel parameter, and optimizing the two parameters (C, σ2 ) by using grid search in the search region. The result of the experiment shows that this SVM method has better performances than the improved back-propagation neural network (BPNN) method in terms of stability, correct classification and the running speed.

关键词： SVM PCA sand-dust storm forecast BPNN

作者: ZHI-YING LU QI-MENG ZHANG ZHI-CHAO ZHAO

作者单位: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3677-3681

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）