【作者】 熊伟丽； 徐保国；

【Author】 XIONG Wei-li XU Bao-guo School of Communication and Control Engineering,Southern Yangtze University,Wuxi 214122,China.

【机构】 江南大学 通信与控制工程学院；

【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,为此提出了基于粒子群算法的 SVR 参数优化选择方法．以不同噪声影响下的 sin c 函数为对象,将提出的基于粒子群算法的参数优化方法与现有的交叉验证法和留一法进行比较．仿真结果表明,粒子群算法优化 SVR 参数方法有效可行,为 SVR 参教优化提供了一种新方法．更多还原

【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 is 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.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.更多还原