NDLI: Multi-parameter prediction for chaotic time series based on least squares support vector regression

Content Provider	IEEE Xplore Digital Library
Author	Ma Jiezhong Liu Yunchao Guo Yangming Zhao Xiaomin
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Northwestern Polytech. Univ., Xi'an, China (Ma Jiezhong; Liu Yunchao; Guo Yangming) \|\| Dept. of Mech. Eng., Univ. of Alberta, Edmonton, AB, Canada (Zhao Xiaomin)
Abstract	Fault prediction is important to the safety and reliability of complex equipments. According to the chaotic characteristic of complex equipments and the prediction theory of support vector machine, a multi-parameter adaptive prediction model is proposed. In order to improve the prediction availability and veracity, the model combines the development and change features of chaotic time series, and obtains the training samples through the phase space reconstruction of multi-parameter time series by referring to considering all informations from the chaotic time series of relative parameters. Prediction experiments are made via simulation of chaotic time series with three parameters of certain complex equipment. The results indicate preliminarily that the model is an effective prediction method for its good prediction precision.
Starting Page	6139
Ending Page	6142
File Size	120019
Page Count	4
File Format	PDF
ISBN	9789881563835
ISSN	19341768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Support vector machines Chaos Time series analysis multi-parameter least squares support vector regression (LS-SVR) Predictive models Educational institutions Data models fault prediction Kernel chaotic time series
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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