| 基于PSO的SVR参数优化选择方法研究 |
| |
| 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 |
| |
| 作者姓名： | 熊伟丽 徐保国 |
| |
| 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 |
| |
| 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。
|
| 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 |
| |
| 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 |
| |
| Authors: | XIONG Wei-li XU Bao-guo |
| |
| Affiliation: | 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 |
| |
| 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. |
| |
| Keywords: | support vector regression parameter selection particle swarm optimization algorithm state estimation |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
