大气科学学报2011，Vol.34Issue(5)：567-573,7.
支持向量机优化的克里金插值算法及其海洋资料对比试验
Kriging interpolation method optimized by Support Vector Machine and its application in oceanic data
摘要
Abstract
The traditional Kriging interpolation method contains only limited types of variogram models to express the spatial variation of variable,which is difficult to depict the distribution of actual data exactly（especially for the spatial structure of complicated data） .A Support Vector Machine-Kriging（SVM-Kriging） method is proposed in this paper by introducing Least Square Support Vector Machine（LS-SVM） to fit the experimental variogram of actual data.The SVM-Kriging method is compared with the traditional Kringing method,and the results show that the variogram of SVM-Kriging method from actual data fields can avoid the subjectivity and arbitrariness of selecting types of variogram models.The SVM-Kriging method has good objectivity and adaptability,effectively improving the Kriging interpolation results.关键词
支持向量机/克里金插值/变异函数/支持向量机—克里金插值算法Key words
Support Vector Machine（SVM）/Kriging interpolation/variogram/SVM-Kriging method分类
海洋科学引用本文复制引用
王辉赞,张韧,刘巍,刘科峰,王桂华..支持向量机优化的克里金插值算法及其海洋资料对比试验[J].大气科学学报,2011,34(5):567-573,7.基金项目
国家自然科学基金资助项目 （）
中加国际科技合作项目 （）
国家重点基础研究发展计划项目 （）
