Combination kernel function least squares support vector machine for chaotic time series prediction | |
Alternative Title | Combination kernel function least squares support vector machine for chaotic time series prediction |
Tian ZhongDa1; Gao XianWen2; Shi Tong3 | |
2014 | |
Source Publication | ACTA PHYSICA SINICA
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ISSN | 1000-3290 |
Volume | 63Issue:16 |
Abstract | Considering the problem that least squares support vector machine prediction model with single kernel function cannot significantly improve the prediction accuracy of chaotic time series, a combination kernel function least squares support vector machine prediction model is proposed. The model uses a polynomial function and radial basis function to construct the kernel function of least squares support vector machine. An improved genetic algorithm with better convergence speed and precision is proposed for parameter optimization of prediction model. The simulation experimental results of Lorenz, Mackey-Glass, Sunspot-Runoff in the Yellow River and chaotic network traffic time series demonstrate the effectiveness and characteristics of the proposed model. |
Keyword | chaotic time series least squares support vector machine combination kernel function improved genetic algorithm |
Indexed By | CSCD |
Language | 英语 |
Funding Project | [National Natural Science Foundation of China] |
CSCD ID | CSCD:5222172 |
Citation statistics |
Cited Times:14[CSCD]
[CSCD Record]
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Document Type | 期刊论文 |
Identifier | http://ir.imr.ac.cn/handle/321006/153551 |
Collection | 中国科学院金属研究所 |
Affiliation | 1.沈阳大学 2.东北大学 3.中国科学院金属研究所 |
Recommended Citation GB/T 7714 | Tian ZhongDa,Gao XianWen,Shi Tong. Combination kernel function least squares support vector machine for chaotic time series prediction[J]. ACTA PHYSICA SINICA,2014,63(16). |
APA | Tian ZhongDa,Gao XianWen,&Shi Tong.(2014).Combination kernel function least squares support vector machine for chaotic time series prediction.ACTA PHYSICA SINICA,63(16). |
MLA | Tian ZhongDa,et al."Combination kernel function least squares support vector machine for chaotic time series prediction".ACTA PHYSICA SINICA 63.16(2014). |
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