@inproceedings{da1b76359da64d9db0ed13bd3a72304f,
title = "Multi-parameter prediction for chaotic time series based on least squares support vector regression",
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.",
keywords = "chaotic time series, fault prediction, least squares support vector regression (LS-SVR), multi-parameter",
author = "Jiezhong Ma and Yunchao Liu and Yangming Guo and Xiaomin Zhao",
year = "2013",
month = oct,
day = "18",
language = "英语",
isbn = "9789881563835",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6139--6142",
booktitle = "Proceedings of the 32nd Chinese Control Conference, CCC 2013",
note = "32nd Chinese Control Conference, CCC 2013 ; Conference date: 26-07-2013 Through 28-07-2013",
}