| 基于矢量基学习的自适应迭代最小二乘支持向量机回归算法 |
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| 引用本文： | 邢永忠,吴晓蓓,徐志良.基于矢量基学习的自适应迭代最小二乘支持向量机回归算法[J].南京理工大学学报(自然科学版),2011,35(3). |
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| 作者姓名： | 邢永忠 吴晓蓓 徐志良 |
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| 作者单位： | 1. 南京理工大学,自动化学院,江苏南京210094;吉林白城兵器试验中心,吉林白城137001
2. 南京理工大学,自动化学院,江苏南京210094 |
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| 摘 要： | 为增强最小二乘支持向量机(LS-SVM)回归建模的稀疏性、鲁棒性和实时性,在加权LS-SVM的基础上,提出了基于矢量基学习的自适应迭代回归算法。在训练过程中,该算法通过矢量基学习和自适应迭代相结合的方法得到1个小的支持向量集,同时采用加权方法确定权值以减小训练样本中非高斯噪声的影响。回归学习和动态系统辩识的仿真结果表明:在回归建模精度相似的情况下,该算法确定的支持向量为全部学习样本的4.9%~8.9%,训练时间为标准LS-SVM的0.011%~0.383%;由于能够鲁棒跟踪时变非线性系统的动态特性,适合在线实时训练;可进一步用于非线性系统的建模和实时控制研究。
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| 关 键 词： | 最小二乘支持向量机 矢量基 自适应迭代 回归算法 |
Adaptive Iterative LS-SVM Regression Algorithm Based on Vector Base Learning |
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| XING Yong-zhong,WU Xiao-bei,XU Zhi-liang.Adaptive Iterative LS-SVM Regression Algorithm Based on Vector Base Learning[J].Journal of Nanjing University of Science and Technology(Nature Science),2011,35(3). |
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| Authors: | XING Yong-zhong WU Xiao-bei XU Zhi-liang |
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| Institution: | XING Yong-zhong1,2,WU Xiao-bei1,XU Zhi-liang1(1.School of Automation,NUST,Nanjing 210094,China,2.Baicheng Weapons Test Center of China,Baicheng 137001,China) |
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| Abstract: | To enhance the sparseness,robustness and real-time performance for regression modeling of least square support vector machine(LS-SVM),an adaptive iterative regression algorithm based on the vector base learning and weighted LS-SVM is proposed.In the algorithm ' s training process,the vector base learning and adaptive iterative procedures are combined,and a small support vector set can be obtained.The weights are determined by a weighted method in order to reduce the effect of the non-Gaussian noise in train... |
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| Keywords: | least square support vector machine vector base adaptive iterative regression algorithm |
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