Volume 43 Issue 2
Feb.  2017
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SUN Zebin, ZHAO Qi, ZHAO Hongbo, et al. An SVR based hybrid modeling method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(2): 352-359. doi: 10.13700/j.bh.1001-5965.2016.0319(in Chinese)
Citation: SUN Zebin, ZHAO Qi, ZHAO Hongbo, et al. An SVR based hybrid modeling method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(2): 352-359. doi: 10.13700/j.bh.1001-5965.2016.0319(in Chinese)

An SVR based hybrid modeling method

doi: 10.13700/j.bh.1001-5965.2016.0319
Funds:

National Basic Research Program of China 

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  • Corresponding author: ZHAO Hongbo, E-mail:bhzhb@buaa.edu.cn
  • Received Date: 19 Apr 2016
  • Accepted Date: 29 Apr 2016
  • Publish Date: 20 Feb 2017
  • As computing power increases in recent years, data-driven modeling method receives much attention. Modeling methods to analyze quantitative behavior of systems with single mode have been researched much. However, most systems have multiple modes which own different continuous behavior and are influenced by continuous state when switching. This paper proposes the empirical probabilistic hybrid automata model and the qualitative and quantitative hybrid modeling method based on support vector regression (SVR).First, switching points between modes are recognized via wavelet and then the SVR sub-models are constructed for each mode. Finally, all sub-models are integrated within D-Markov machine. The example verification results demonstrate that the proposed method is as stable as traditional SVR model, and much more accurate than it.

     

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