| 基于PSO的SVR参数优化选择方法研究 |
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| 引用本文： | 熊伟丽,徐保国.基于PSO的SVR参数优化选择方法研究[J].系统仿真学报,2006,18(9):2442-2445. |
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| 作者姓名： | 熊伟丽 徐保国 |
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| 作者单位： | 1. 江南大学控制科学与工程研究中心,无锡,214122
2. 江南大学通信与控制工程学院,无锡,214122 |
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| 摘 要： | 支持向量回归机(SVR)模型的拟合精度和泛化能力取决于其相关参数的选取,因此提出了基于粒子群(PSO)算法的SVR参数优化选择方法;并以不同噪声影响下的sinc函数和实际发酵过程产物浓度的SVR模型为对象,将提出的PSO优化参数方法与现有的交叉验证法、留一法进行比较。仿真结果表明:该PSO优化SVR参数方法可行、有效,由此得到的SVR模型具有更好的学习精度和推广能力。
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| 关 键 词： | 支持向量回归 参数优化选择 粒子群算法 状态预估 |
| 文章编号： | 1004-731X（2006）09-2442-04 |
| 收稿时间： | 2006-01-06 |
| 修稿时间： | 2006-06-28 |
Study on Optimization of SVR Parameters Selection Based on PSO |
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| XIONG Wei-li,XU Bao-guo.Study on Optimization of SVR Parameters Selection Based on PSO[J].Journal of System Simulation,2006,18(9):2442-2445. |
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| Authors: | XIONG Wei-li XU Bao-guo |
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| Institution: | 1.Control Science and Engineering Research Center, Southern Yangtze University, Wuxi 214122, China; 2.School of Communication and Control Engineering, Southern Yangtze University, Wuxi 214122, China |
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| Abstract: | The regression accuracy and generalization performance of the support vector regression (SVR) models depend on a proper setting of its parameters. An optimal selection approach of SVR parameters was put forward based on particle swarm optimization (PSO) algorithm. Furthermore, a comparison was made between the performance of PSO parameter selection and cross validation (CV) and leave-one-out (LOO) method on various data sets, such as a sin c function with additive noise and a SVR model of the product concentration in fermentation process. Simulation results show that the optimal selection approach based on PSO is available and the PSO-SVR model has superior learning accuracy and generalization performance. |
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| Keywords: | support vector regression parameter selection particle swarm optimization algorithm state estimation |
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