TY - GEN
T1 - Improved multi-kernel LS-SVR for time series online prediction with incremental learning
AU - Guo, Yangming
AU - Wang, Xiangtao
AU - Zheng, Yafei
AU - Liu, Chong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/2/9
Y1 - 2015/2/9
N2 - Since it is difficult to establish precise physical model of complex systems, time series prediction is often used to predict their health trend and running state. Aiming at online prediction, we proposed a new scheme to fix the problems of time series online prediction, which is based on LS-SVR model and incremental learning algorithm. The scheme includes two aspects. Firstly, by replacing single kernel with new fixed kernel consisting of several basis kernels, a better information mapping in high dimension is obtained; secondly, by establishing new LS-SVR model without bias term b, the calculation process with incremental learning is simplified. Prediction experiment is performed via certain avionics application. The results indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.
AB - Since it is difficult to establish precise physical model of complex systems, time series prediction is often used to predict their health trend and running state. Aiming at online prediction, we proposed a new scheme to fix the problems of time series online prediction, which is based on LS-SVR model and incremental learning algorithm. The scheme includes two aspects. Firstly, by replacing single kernel with new fixed kernel consisting of several basis kernels, a better information mapping in high dimension is obtained; secondly, by establishing new LS-SVR model without bias term b, the calculation process with incremental learning is simplified. Prediction experiment is performed via certain avionics application. The results indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.
KW - Incremental learning algorithm
KW - Least Squares Support Vector Regression (LS-SVR)
KW - Multiple kernel learning (MKL)
KW - Online prediction
KW - Time series
UR - https://www.scopus.com/pages/publications/84929590725
U2 - 10.1109/ICPHM.2014.7036376
DO - 10.1109/ICPHM.2014.7036376
M3 - 会议稿件
AN - SCOPUS:84929590725
T3 - 2014 International Conference on Prognostics and Health Management, PHM 2014
BT - 2014 International Conference on Prognostics and Health Management, PHM 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Prognostics and Health Management, PHM 2014
Y2 - 22 June 2014 through 25 June 2014
ER -