| 支持向量机控制与在线学习方法研究的进展 |
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| 引用本文： | 王定成,姜斌.支持向量机控制与在线学习方法研究的进展[J].系统仿真学报,2007,19(6):1177-1181. |
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| 作者姓名： | 王定成 姜斌 |
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| 作者单位： | 1. 南京信息工程大学计算机与软件学院,江苏南京,210044;南京航空航天大学自动化学院,江苏南京,210016
2. 南京航空航天大学自动化学院,江苏南京,210016 |
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| 基金项目： | 国家自然科学基金;江苏省高校自然科学基金 |
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| 摘 要： | 支持向量机以其模型结构简单、较好的推广能力和全局最优解等特点已经被用来进行智能控制的研究,主要包括采用支持向量机回归的非线性时间序列的建模与预测、系统辨识等建模方面的研究以及优化控制、学习控制和预测控制等方面的研究以及采用支持向量机的故障诊断的研究。由于现有SVMR基于二次规划的优化方法不适合控制过程的在线训练,因此出现了对SVMR在线训练算法的研究。分析了国内外这些研究内容的最新研究进展,旨在探讨归纳支持向量机在控制领域研究的主要成果和存在的问题,以便为进一步的研究提供一定的支持与帮助。
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| 关 键 词： | 支持向量机回归(SVMR) 建模 控制 在线 |
| 文章编号： | 1004-731X（2007）06-1177-05 |
| 收稿时间： | 2006-02-13 |
| 修稿时间： | 2006-11-04 |
Review of SVM-based Control and Online Training Algorithms |
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| WANG Ding-cheng,JIANG Bin.Review of SVM-based Control and Online Training Algorithms[J].Journal of System Simulation,2007,19(6):1177-1181. |
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| Authors: | WANG Ding-cheng JIANG Bin |
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| Institution: | 1.Institute of Computer and Software, Nanj ing University of Information Seienee and Technology, Nanj ing 210044, China; 2.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: | The support vector machines, which characterizes a simple model structure, good generalization, global optimal solution etc., has been applied in the intelligent control. The current research includes SVMR modeling and prediction of nonlinear times serial, system identification, optimal control, learning control, predict control etc., and SVM-based fault diagnosis. Since SVMR based on the quadratic programming unfits for the online training of control, the current research includes the online training of SVMR. The latest development of those contents in the domestic and overseas research were analyzed, and the main achievements and problems in SVMR control were summed up in order to provide some support and help for the further research. |
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| Keywords: | support vector machines regression (SVMR) modeling control online |
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