中南大学学报（自然科学版）2011，Vol.42Issue(4)：1000-1004,5.
基于混合QPSO的LS-SVM参数优化及其应用
Hybrid-QPSO-based parameters optimization of LS-SVM and its application
摘要
Abstract
Aiming at the parameter optimization problem in least squares support vector machine, a hybrid QPSO algorithm for LS-SVM parameter selection was proposed to improve the learning performance and generalization ability of LS-SVM model. The Powell algorithm was used to obtain the initial position, local optimal position and global optimal solution. The proposed hybrid QPSO method combines with the global search ability of QPSO algorithm with the local search ability of Powell algorithm, to improve the solving speed and the accuracy of the solution. This modeling method was validated by test function firstly and compared with the PSO LS-SVM model. Then the production data from a purification process of zinc hydrometallurgy was used to test the model precision. The test results show that the proposed model has better generalization performance and higher precision.关键词
最小二乘支持向量机/参数优化/HQPSO算法/净化过程Key words
LS-SVM/ parameters optimization/ hybrid QPSO/ purification process分类
信息技术与安全科学引用本文复制引用
朱红求,阳春华,王觉,桂卫华..基于混合QPSO的LS-SVM参数优化及其应用[J].中南大学学报（自然科学版）,2011,42(4):1000-1004,5.基金项目
国家高技术研究发展计划("863"计划)项目(2009AA04Z124) （"863"计划）
国家自然科学基金资助项目(61025015,60874069) （61025015,60874069）
湖南省自然科学基金资助项目(09JJ3122) （09JJ3122）
