Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

Xin You Wang; Guo Fei Gao; Zhan Qu; Hai Feng Pu

doi:10.4028/www.scientific.net/AMR.1061-1062.935

Paper Titles

A Semantic Gateway for Home Automation Networks
p.916

Analysis and Control of the Fault in the Hydraulic System of Coal-Machinery
p.921

Quasi-Close Loop Control of NC Machine Tools
p.927

Research Situation of Deicing Device of Transmission Line Deicing Robot
p.931

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines
p.935

Manipulator Tactile Telepresence Research Based on one Type Pressure-Sensitive Sensor
p.939

Design of Multi Parameter Water Quality Online Monitoring System Base on FPGA
p.945

Expert System Based on Fuzzy Rules for Monitoring and Diagnosis of Operation Conditions in Rotating Machines
p.950

Properties of Films Characterized by Scanning Acoustic Microscope
p.961

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1061-1062Study on Prediction of Chaotic Time Series Using...

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

Abstract:

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

You might also be interested in these eBooks

Research in Materials and Manufacturing Technologies IV

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1061-1062)

Pages:

935-938

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1061-1062.935

Citation:

Cite this paper

Online since:

December 2014

Authors:

Xin You Wang*, Guo Fei Gao, Zhan Qu, Hai Feng Pu

Keywords:

Chaotic Time Series Prediction, Least Squares Support Vector Machines (Ls-SVM), Machine Learning

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., and Vandewalle, J., Least Squares Support Vector Machines, World Scientific, Singapore, (2002).

DOI: 10.1142/5089

Google Scholar

[2] BIAN Zhaoqi. Pattern Recognition (in Chinese). Beijing: Tsinghua University Press, 1988: 295-296.

Google Scholar

[3] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999, 9(3): 293- 300.

Google Scholar

[4] Pabitra Mitra, B. Uma Shankar, Sankar K. Pal. Segmentation of Multispectral Remote Sensing Images Using Active Support Vector Machines. Pattern Recongnition Letters, 2004, 25: 1067- 1074.

DOI: 10.1016/j.patrec.2004.03.004

Google Scholar

[5] REYNA RA, CATTEONM. Segmenting Images with Support Vector Machines[A]. IEEE International Conference on Image Processing[C]. 2000 . 820- 823.

Google Scholar

[6] Information on http: /www. east. kuleuven. ac. be/sista/lssvmlab.

Google Scholar