Gross Industrial Output Value Prediction Based on Least Squares Support Vector Regression

摘要：

Least squares support vector regression is presented in gross industrial output value prediction in the paper. Least squares support vector regression is a kind modified support vector regression. It can solve a convex quadratic programming problem, which has higher performance than support vector regression. The data of gross industrial output value in Fujian province from 1990 to 2006 are employed to train and test the proposed model. It is indicated that prediction performance of gross industrial output value of LSSVR model is best in the RBFNN, SVR and LSSVR prediction model. Then, LSSVR has very high application values in prediction of gross industrial output value.

关键词： Least squares support vector regression prediction performance gross industrial output value

作者: Gang Long

作者单位: Economics and Management School Wuhan University Wuhan 430072, China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 2226-2229

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）