Least Square Support Vector Machine Based on Improved Particle Swarm Optimization to Short-term Forecasting

摘要：

Forecasting based on least squares support vector machine (LS-SVM) method can be a very good track historical data, and there have a good predictive ability of extrapolation. However, parameter selection is an import work in the application of LS-SVM as it is related to the performance of the constructed predicting. Therefore, an improved particle swarm optimization (IPSO) algorithm was proposed to optimize parameters selection, IPSO for selecting the global optimum parameters of LS-SVM automatically, and avoiding the defects of premature convergence of PSO algorithm. The empirical results show that the improved approach has a better performance and is more effective than other approaches.

关键词： Forecasting LS-SVM Parameter IPSO Empirical

作者: Dabin Zhang Sen Peng Yuting Duan Wensheng Zhang

作者单位: Department of Information Management,Central China Normal University,Wuhan, 430079, China Institute Department of Information Management,Central China Normal University,Wuhan, 430079, China Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Business Intelligence and Financial Engineering(第四届商务智能与金融工程国际会议 BIFE2011)

会议地点: 武汉

会议语种:英文

页码: 45-48

在线出版日期: 2011-10-17（万方平台首次上网日期，不代表论文的发表时间）