CAJ | 학술논문

支持向量机作为一个新兴的数学建模工具已经被广泛地应用到很多工业控制领域中,其良好的泛化能力和预测精度在很大程度上受到其参数选取的影响.根据智能群体进化模式改进粒子群优化算法.利用模糊C均值聚类算法分类粒子群体,并用子群体最优点取代速度更新公式中的个体历史最优点,并利用该算法搜索支持向量机的最优参数组合.对比仿真实验表明:所提优化算法是支持向量机参数选取的有效算法,在非线性函数估计中体现出优良的性能.
지지향량궤작위일개신흥적수학건모공구이경피엄범지응용도흔다공업공제영역중,기량호적범화능력화예측정도재흔대정도상수도기삼수선취적영향.근거지능군체진화모식개진입자군우화산법.이용모호C균치취류산법분류입자군체,병용자군체최우점취대속도경신공식중적개체역사최우점,병이용해산법수색지지향량궤적최우삼수조합.대비방진실험표명:소제우화산법시지지향량궤삼수선취적유효산법,재비선성함수고계중체현출우량적성능.
Support Vector Machine(SVM), a new mathematic modeling tool, has been widely used in many industry applications. The good generalization ability and estimation accuracy are impacted by parameters selection of SVM. The Particle Swarm Optimization (PSO) was improved based on evolution model of intelligence group. The whole group was divided into small groups by fuzzy C-means clustering algorithm. The individual best points in velocity updating function were replaced by the best points in small groups. At last, the extended PSO algorithm was proposed to search the optimal combination of SVM parameters. Simulations show that the proposed algorithm is an effective way to search the SVM parameters and has good performance in nonlinear function estimation.