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Content Provider | IEEE Xplore Digital Library |
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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 | |
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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