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Combination kernel function least squares support vector machine for chaotic time series prediction
Alternative TitleCombination kernel function least squares support vector machine for chaotic time series prediction
Tian ZhongDa1; Gao XianWen2; Shi Tong3
2014
Source PublicationACTA PHYSICA SINICA
ISSN1000-3290
Volume63Issue:16
AbstractConsidering 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.
Keywordchaotic time series least squares support vector machine combination kernel function improved genetic algorithm
Indexed ByCSCD
Language英语
Funding Project[National Natural Science Foundation of China]
CSCD IDCSCD:5222172
Citation statistics
Cited Times:12[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/153551
Collection中国科学院金属研究所
Affiliation1.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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