引用本文:陈爱军,宋执环,李平.基于矢量基学习的最小二乘支持向量机建模[J].控制理论与应用,2007,24(1):1~5.[点击复制]
CHEN Ai-jun ,SONG Zhi-huan,LI Ping.Modeling method of least squares supportvector regression based on vector base learning[J].Control Theory and Technology,2007,24(1):1~5.[点击复制]
基于矢量基学习的最小二乘支持向量机建模
Modeling method of least squares supportvector regression based on vector base learning
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DOI编号  
  2007,24(1):1-5
中文关键词  最小二乘支持向量机  矢量基  稀疏性  增长记忆模式  支持向量
英文关键词  least square support vector machine  vector base  sparsity  increased memory mode  support vector
基金项目  国家“863”计划资助项目 2003AA412110
作者单位
陈爱军,宋执环,李平 1. 浙江大学工业控制技术研究所,浙江杭州 310027
2. 中国石油兰州石化公司自动化研究院, 甘肃兰州 730060 
中文摘要
      为使最小二乘支持向量机的解具有稀疏性, 本文提出了一种稀疏解算法--矢量基学习. 首先引入基矢量、基矢量集与矢量空间的概念, 并分析新样本矢量与矢量空间的夹角, 从而推导出该样本是否为基矢量的判断准则. 随着新样本的到来, 在线判别支持向量, 使LS-SVM的支持向量具有稀疏性. 提升LS-SVM动态建模的实时性, 本文进一步提出用于矢量基学习的增长记忆模式递推公式. 仿真分析及水处理厂的应用实例, 验证了该方法的可行性和有效性.
英文摘要
      To achieve a sparse solution for least squares support vector regression (LS-SVM), an algorithm called vector base learning (VBL) is proposed in this paper. Firstly, the concepts of base vector (BV), base vector set (BVS) and vector space are introduced. By calculating the angle between the new sample vector and the vector space, the criteria for determining whether the measurement vector is one of the BVS is then derived. This determination is carried out on-line for the coming new samples. This makes the solutions of LS-SVM having the feature of sparsity. To improve the modeling speed of LS-SVM, a recursive algorithm of increased memory mode for VBL algorithm is also proposed. Finally, simulation analysis and the modeling of a typical plant for water treatment clearly illustrated the validity and feasibility of the presented method.