文献类型：期刊文章

作　　者：朱家元[1] 段宝君[1] 张恒喜[1]

机构地区：[1]空军工程大学工程学院飞机与发动机工程系,西安710038

出　　处：《计算机科学》

基　　金：国防预研资助基金(项目编号:98J19.3.2.JB3201); 空军重点型号工程课题

年　　份：2003

卷　　号：30

期　　号：8

起止页码：124-125

语　　种：中文

收录情况：BDHX、BDHX2000、CSA、CSCD、CSCD2011_2012、IC、JST、RCCSE、UPD、ZGKJHX、核心刊

摘　　要：In this paper, we present a new support vector machines-least squares support vector machines (LS-SVMs). While standard SVMs solutions involve solving quadratic or linear programming problems, the least squaresversion of SVMs corresponds to solving a set of linear equations, due to equality instead of inequality constraints inthe problem formulation. In LS-SVMs, Mercer condition is still applicable. Hence several type of kernels such aspolynomial, RBF's and MLP's can be used. Here we use LS-SVMs to time series prediction compared to radial basisfunction neural networks. We consider a noisy (Gaussian and uniform noise)Mackey-Glass time series. The resultsshow that least squares support vector machines is excellent for time series prediction even with high noise.

关 键 词：机器学习  支持向量机 SVM 时间序列预测 模糊神经网络

分 类 号：TP181]