基于PSO的SVR参数优化选择方法研究

Study on Optimization of SVR Parameters Selection Based on PSO

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。

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.