| 基于支持向量机的系统辨识 |
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| 引用本文： | 翟永杰,王国鹏,韩璞,王东风. 基于支持向量机的系统辨识[J]. 计算机仿真, 2004, 21(11): 39-43 |
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| 作者姓名： | 翟永杰 王国鹏 韩璞 王东风 |
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| 作者单位： | 华北电力大学动力工程系,河北,保定,071003;华北电力大学动力工程系,河北,保定,071003;华北电力大学动力工程系,河北,保定,071003;华北电力大学动力工程系,河北,保定,071003 |
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| 摘 要： | 支持向量机是在统计学习理论基础上发展的一种新的机器学习方法，由于其出色的学习性能，该技术已成为当前国际机器学习界的研究热点。该文利用支持向量机，选取不同的核函数，分别对线性自回归滑动平均模型、双线性模型、非线性模型进行模型辨识。仿真结果显示该方法具有良好的辨识性能。
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| 关 键 词： | 支持向量机 系统辨识 核函数 |
| 文章编号： | 1006-9348(2004)11-0039-03 |
| 修稿时间： | 2003-05-13 |
System Identification Based on Support Vector Machine |
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| ZHAI Yong-jie,WANG Guo-peng,HAN Pu,WANG Dong-feng. System Identification Based on Support Vector Machine[J]. Computer Simulation, 2004, 21(11): 39-43 |
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| Authors: | ZHAI Yong-jie WANG Guo-peng HAN Pu WANG Dong-feng |
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| Abstract: | Support Vector Machine is a new kind of machine learning algorithm based on the statistical learning theory. Because of its standout learning capability, the algorithm has been one of the hot points in international machine learning research. In this paper, Support Vector Machine is presented by choosing different kernel function. In this way, ARMA model, BM model and NARMA model are identified. The favorable identification capability of Support Vector Machines is proved through simulation. |
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| Keywords: | Support vector machine System identification Kernel function |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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