IFS-LSSVM and its application in time-delay series prediction

被引：0

作者：

Tian, Zhong-Da ^{[1
]}

Li, Shu-Jiang ^{[1
]}

Wang, Yan-Hong ^{[1
]}

Gao, Xian-Wen ^{[2
]}

机构：

[1] College of Information Science and Engineering, Shenyang University of Technology, Shenyang

[2] College of Information Science and Engineering, Northeastern University, Shenyang

来源：

Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2015年 / 19卷 / 11期

关键词：

Free search; Least squares support machines; Prediction; Time series; Time-delay series;

D O I：

10.15938/j.emc.2015.11.016

中图分类号：

学科分类号：

摘要：

It is difficult to determine the optimal parameters of least squares support vector machine prediction model, so a prediction method based on improved free search algorithm (IFS-LSSVM) was proposed to determine the optimal parameters of least squares support vector machines. First, the standard free search algorithm was improved so that it can be applied to the parameter optimization of least squares support vector machines, the improved harmony search algorithm has better optimization performance. Then the least squares support vector machines was applied to predict the time-delay series of the network based on improved free search optimization algorithm. Finally, time-delay series was used as prediction simulation object, genetic algorithm optimized least squares support vector machines (GA-LSSVM), particle swarm optimization algorithm optimized least squares support vector machines (PSO-LSSVM), standard grid search method of least squares support vector machines (Grid-LSSVM) toolbox were compared. Simulation comparison results show that the proposed method has higher prediction accuracy and smaller prediction error. ©, 2015, Editorial Department of Electric Machines and Control. All right reserved.

引用

页码：104 / 110

页数：6

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