An SVR based hybrid modeling method

被引：0
|
作者：
Sun Z. [1 ]
Zhao Q. [1 ]
Zhao H. [1 ]
Feng W. [1 ]
Zhang W. [1 ]
Yang T. [2 ]
机构：
[1] School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing
[2] State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an
来源：
Zhao, Hongbo (bhzhb@buaa.edu.cn) | 2017年 / Beijing University of Aeronautics and Astronautics (BUAA)卷 / 43期
关键词：
D-Markov machine; Data-driven modeling; Hybrid modeling; Support vector regression (SVR); Wavelet;
D O I：
10.13700/j.bh.1001-5965.2016.0319
中图分类号：
学科分类号：
摘要：
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. © 2017, Editorial Board of JBUAA. All right reserved.
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页码：352 / 359
页数：7
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