Online support vector regression for system identification

Authors:
Zhenhua Yu;Xiao Fu;Yinglu Li
Affiliations:
School of Telecommunication Engineering, Air Force Engineering University, Xi'an, China;School of Telecommunication Engineering, Air Force Engineering University, Xi'an, China;School of Telecommunication Engineering, Air Force Engineering University, Xi'an, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 4
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Abstract

Conventional Support Vector Regression (SVR) is not capable of online setting and its training algorithm is inefficient in real-time applications. Through analyzing the possible variation of support vector sets after new samples are added to the training set, and extending the incremental support vector machine for classification, an online learning algorithm for SVR is proposed. To illustrate the favorable performance of the online learning algorithm, a nonlinear system identification experiment is considered. The simulation results indicate that the learning efficiency and prediction accuracy of the online learning algorithm are higher than that of the existing algorithms, and it is more suitable for system identification.