计算机工程与应用Issue(4)：133-137,5.DOI:10.3778/j.issn.1002-8331.1303-0459
基于混合核函数FOA-LSSVM的预测模型
Forecasting model via LSSVM with mixed kernel and FOA
摘要
Abstract
The kernel and the parameters of Support Vector Machine(SVM)have a significant impact on precision. In view of better learning capability of local kernels and better generalization capability of global kernels, the mixed kernel is constructed by a typical local kernel-Radial Basis Function(RBF)and a typical global kernel-polynomial kernel. By use of Fruit Fly Optimization Algorithm(FOA), a novel FOA-LSSVM model with mixed kernels is set up in this paper. Results demonstrate that the new model has great accuracy than traditional methods and has real application value in forecasting.关键词
预测/果蝇优化算法(FOA)/最小二乘支持向量机(LSSVM)/混合核Key words
forecasting/Fruit Fly Optimization Algorithm/Least Square Support Vector Machine/mixed kernel分类
信息技术与安全科学引用本文复制引用
周金明,王传玉,何帮强..基于混合核函数FOA-LSSVM的预测模型[J].计算机工程与应用,2015,(4):133-137,5.基金项目
国家自然科学基金（No.10826098，No.71171003）；安徽工程科技学院青年基金资助项目（No.2008YQ038）；安徽省自然科学基金资助项目（No.090416225）；安徽高校自然科学基金资助项目（No.KJ2010A037）。 （）
