Online independent reduced least squares support vector regression

Authors:
Yong-Ping Zhao;Jian-Guo Sun;Zhong-Hua Du;Zhi-An Zhang;Ye-Bo Li
Affiliations:
ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nangjing 210094, PR China;College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nangjing 210094, PR China;ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nangjing 210094, PR China;College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
Venue:
Information Sciences: an International Journal
Year:
2012

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Abstract

In this paper, an online algorithm, viz. online independent reduced least squares support vector regression (OIRLSSVR), is proposed based on the linear independence and the reduced technique. As opposed to some offline algorithms, OIRLSSVR takes the realtime advantage, which is confirmed using benchmark data sets. In comparison with online algorithm, the realtime of OIRLSSVR is also favorable. As for this point, it is tested with experiments on the benchmark data sets and a more realistic scenario namely a diesel engine example. All in all, OIRLSSVR can enhance the modeling realtime, especially for the case where the samples enter in a flow mode.