| 稀疏最小二乘支持向量机及其应用 |
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| 引用本文： | 衷路生,陈立勇.稀疏最小二乘支持向量机及其应用[J].中国科技论文在线,2014(7):779-783. |
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| 作者姓名： | 衷路生 陈立勇 |
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| 作者单位： | 华东交通大学电气与电子工程学院,南昌330013 |
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| 基金项目： | 国家自然科学基金资助项目（60904049,61263010）;江西省自然科学基金资助项目（20114BAB211014）;江西省教育厅科学技术研究项目（GJJl4899）;国家留学基金资助项目 |
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| 摘 要： | 提出基于特征向量选择(feature vector selection,FVS)的稀疏最小二乘支持向量机(sparse least squares support vector machine,SLS-SVM)模型,解决最小二乘支持向量机(least squares support vector machine,LS-SVM)稀疏化问题。采用FVS在特征空间构建特征向量子集,对训练样本进行稀疏线性重构;将稀疏化的特征向量作为支持向量,从而实现对LS-SVM稀疏化建模。将SLS-SVM模型进行弓网系统的仿真对比实验,结果表明SLS-SVM模型在取得高预报精度的同时,可实现支持向量的高度稀疏化,从而加快模型预报速度。
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| 关 键 词： | 特征向量 稀疏 支持向量 弓网系统 |
Application of sparse least squares support vector machine |
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| Zhong Lusheng,Chen Liyong.Application of sparse least squares support vector machine[J].Sciencepaper Online,2014(7):779-783. |
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| Authors: | Zhong Lusheng Chen Liyong |
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| Institution: | (School of Electrical and Electronic Engineering, East China J iaotong University, Nanchang 330013, China) |
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| Abstract: | A new model of sparse least squares support vector machine （SLS-SVM）is proposed to solve the sparseness problem of least squares support vector machine （LS-SVM）,based on feature vector selection （FVS）method.A subset of feature vectors is defined in feature space to reconstruct all the training samples linearly.The sparse feature vectors are used as support vectors to model LS-SVM.SLS-SVM is simulated with pantograph-catenary system.It is shown that SLS-SVM can improve forecast preci-sion,and accelerate prediction speed by achieving highly sparse support vectors. |
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| Keywords: | feature vector sparse support vector pantograph-catenary system |
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