| 在线鲁棒最小二乘支持向量机回归建模 |
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| 引用本文： | 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 |
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| 作者姓名： | 张淑宁 王福利 何大阔 贾润达 |
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| 作者单位： | 1. 东北大学信息科学与工程学院,辽宁沈阳,110004
2. 东北大学信息科学与工程学院,辽宁沈阳110004／东北大学流程工业综合自动化国家重点实验室,辽宁沈阳110004 |
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| 基金项目： | 国家“863”高技术研究发展计划资助项目(2006AA060201); 国家自然科学青年基金资助项目(61004083); 中央高校基本科研业务费资助项目(N100604008). |
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| 摘 要： | 鉴于工业过程的时变特性以及现场采集的数据通常具有非线性特性且包含离群点,利用最小二乘支持向量机回归（least squares support vector regression,LSSVR）建模易受离群点的影响.针对这一问题,结合鲁棒学习算法（robust learning algorithm,RLA）,本文提出了一种在线鲁棒最小二乘支持向量机回归建模方法.该方法首先利用LSSVR模型对过程输出进行预测,与真实输出相比较得到预测误差;然后利用RLA方法训练LSSVR模型的权值,建立鲁棒LSSVR模型;最后应用增量学习方法在线更新鲁棒LSSVR模型,从而得到在线鲁棒LSSVR模型.仿真研究验证了所提方法的有效性.
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| 关 键 词： | 鲁棒学习算法 最小二乘支持向量机 鲁棒性 非线性 |
| 收稿时间： | 2010-04-09 |
| 修稿时间： | 2010-12-03 |
Modeling method of online robust least-squares-support-vector regression |
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| ZHANG Shu-ning,WANG Fu-li,HE Da-kuo and JIA Run-da. Modeling method of online robust least-squares-support-vector regression[J]. Control Theory & Applications, 2011, 28(11): 1601-1606 |
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| Authors: | ZHANG Shu-ning WANG Fu-li HE Da-kuo JIA Run-da |
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| Affiliation: | School of Information Science & Engineering, Northeastern University,School of Information Science & Engineering, Northeastern University; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University,School of Information Science & Engineering, Northeastern University; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University,School of Information Science & Engineering, Northeastern University |
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| 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. |
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| Keywords: | robust learning algorithm least-squares-support-vector machine robustness nonlinear |
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