控制理论与应用2011，Vol.28Issue(11)：1601-1606,6.
在线鲁棒最小二乘支持向量机回归建模
Modeling method of online robust least-squares-support-vector regression
摘要
Abstract
Industrial processes possess time-varying feature,and data from industrial field usually possess nonlinear feature and contain outliers.Modeling with least-squares-support-vector regression（LSSVR） method may suffer from these outliers.To deal with this problem,we develop an online robust LSSVR method by combining with the robust learning algorithm（RLA）.The LSSVR model is used to predict process outputs,and the residuals are formed from real outputs and predicted outputs.The RLA trains the weights of LSSVR model iteratively.The trained robust LSSVR model is then updated by means of incremental updating algorithm.An online robust LSSVR model is also developed.Simulation results show the effectiveness of the proposed approach.关键词
鲁棒学习算法/最小二乘支持向量机/鲁棒性/非线性Key words
robust learning algorithm/least-squares-support-vector machine/robustness/nonlinear分类
信息技术与安全科学引用本文复制引用
张淑宁,王福利,何大阔,贾润达..在线鲁棒最小二乘支持向量机回归建模[J].控制理论与应用,2011,28(11):1601-1606,6.基金项目
国家“863”高技术研究发展计划资助项目 （）
国家自然科学青年基金资助项目 （）
中央高校基本科研业务费资助项目 （）
