南昌工程学院学报Issue(6)：15-19,5.
最小二乘支持向量机的参数优选方法及其应用
Method for selecting parameters of least squares support vector machines and its application
摘要
Abstract
It is an important issue for support vector machines( SVMs)to choose parameters in engineer-ing applications and theoretic research. By combining the evolution idea of genetic algorithm with the swarm intelligence of particle swarm optimization algorithm,a hybrid intelligent algorithm is developed and applied to water demand forecasting in the paper. It makes use of PSO algorithm characteristics such as parallel property and the global convergence performance to avoid the local optimum,and uses the evolu-tion idea of genetic algorithm such as crossover and mutation operations to improve the speed of searching for the global optimization. On the other hand,a deterministic searching algorithm is embedded to improve its optimization performance,based on which the LS-SVM prediction model of water consumption is pro-posed,tuning LS-SVM parameters by hybrid intelligent algorithm. The application shows that the presented LS-SVM optimized by hybrid intelligent algorithm can offer more accurate forecasting result than SVM method,and has high generalization ability.关键词
最小二乘支持向量机/混合智能算法/参数优选Key words
least squares support vector machines( LS-SVM)/hybrid intelligent algorithm/parameter op-timization分类
建筑与水利引用本文复制引用
刘丽娜..最小二乘支持向量机的参数优选方法及其应用[J].南昌工程学院学报,2014,(6):15-19,5.基金项目
国家自然科学青年基金资助项目（51309130）；江西省科技厅青年基金资助项目（20132BAB213025）；江西省教育厅青年科学基金项目（GJJ11254）；南昌工程学院青年基金项目 （）
