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

Jiezhong Ma, Yunchao Liu, Yangming Guo, Xiaomin Zhao

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Proceedings of the 32nd Chinese Control Conference, CCC 2013
出版商IEEE Computer Society
6139-6142
页数4
ISBN(印刷版)9789881563835
出版状态已出版 - 18 10月 2013
活动32nd Chinese Control Conference, CCC 2013 - Xi'an, 中国
期限: 26 7月 201328 7月 2013

出版系列

姓名Chinese Control Conference, CCC
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议32nd Chinese Control Conference, CCC 2013
国家/地区中国
Xi'an
时期26/07/1328/07/13

指纹

探究 'Multi-parameter prediction for chaotic time series based on least squares support vector regression' 的科研主题。它们共同构成独一无二的指纹。

引用此