| 基于核主元提取的支持向量机辨识 |
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| 引用本文： | 于春梅,潘泉,程咏梅,张洪才.基于核主元提取的支持向量机辨识[J].数学的实践与认识,2009,39(1). |
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| 作者姓名： | 于春梅 潘泉 程咏梅 张洪才 |
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| 作者单位： | 西北工业大学自动化学院,陕西,西安,710072 |
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| 基金项目： | 国家自然科学基金重点项目，高等学校博士学科点专项科研基金 |
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| 摘 要： | 将一种基于特征提取的ε-不灵敏支持向量机方法用于非线性系统辨识.对输入输出数据首先进行核主元特征提取,将特征提取后的数据作为支持向量机的训练数据.将该方法与基于主元特征提取的方法和直接应用ε-不灵敏支持向量机的方法进行含噪和不含噪情况下的仿真比较,结果表明,方法的拟合性能和抗干扰能力优于其他两种方法.
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| 关 键 词： | 支持向量机 非线性系统辨识 核主元分析 特征提取 |
KPCA Based SVM Identification of Nonlinear System |
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| YU Chun-mei,PAN Quan,CHENG Yong-mei,ZHANG Hong-cai.KPCA Based SVM Identification of Nonlinear System[J].Mathematics in Practice and Theory,2009,39(1). |
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| Authors: | YU Chun-mei PAN Quan CHENG Yong-mei ZHANG Hong-cai |
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| Abstract: | A feature extraction based ε-insensitive support vector machine(SVM) method is applied to nonlinear system identification.First,kernel principal component analysis(KPCA) is used to input-output data for feature extraction,which is used as the training data to ε-insensitive SVM.Then,we compare the proposed method with ε-insensitive SVM method and PCA based ε-insensitive SVM.the results show that KPCA based method perform better than ε-insensitive SVM and PCA based ε-insensitive SVM methods. |
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| Keywords: | support vector machine(SVM) nonlinear system identification kernel principal component analysis(KPCA) feature extraction |
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