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基于遗传算法的回归型支持向量机参数选择法 被引量：42

Parameters selection of support vector regression based on genetic algorithm.
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摘要 研究了遗传算法在回归型支持向量机参数选择中的应用:首先,分析了支持向量机的几个参数对其预报能力的影响,发现参数选取不当,会导致支持向量机出现过学习或欠学习现象;在此基础上提出利用遗传算法来解决回归型支持向量机的参数选择问题,模拟实验证明,该方法克服了传统参数选择方法存在的缺点,提高了支持向量机的预报精度。 This paper mainly deals with the application of genetic algorithm to parameters selection of support vector regression. The variability in predict performance of support vector regression with respect to the free parameters is firstly investigated.A conclusion is drawn that improper parameter would lead to overfitting or underfitting.Then the genetic algorithm is introduced to select parameters for support vector regression.Simulation results proved that the proposed method overcame some obstacles of traditional parameter selection methods,thus the predict precision of support vector regression is enhanced.
作者 李良敏 温广瑞 王生昌
机构地区 长安大学汽车运输安全保障技术交通行业重点实验室 西安交通大学机械制造系统工程国家重点实验室 长安大学汽车学院
出处 《计算机工程与应用》 CSCD 北大核心 2008年第7期23-26,共4页 Computer Engineering and Applications
基金 国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2006-AA04Z429)
关键词 回归型支持向量机 遗传算法 参数选择 support vector regression genetic algorithm parameters selection
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献11

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二级参考文献28

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共引文献168

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引证文献42

二级引证文献344

计算机工程与应用
2008年 第7期

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