一种基于支持向量回归的混合建模方法

孙泽斌; 赵琦; 赵洪博; 冯文全; 张文峰; 杨天社

doi:10.13700/j.bh.1001-5965.2016.0319

一种基于支持向量回归的混合建模方法

doi: 10.13700/j.bh.1001-5965.2016.0319

1.
北京航空航天大学电子信息工程学院, 北京 100083
2.
西安卫星测控中心宇航动力学国家重点实验室, 西安 710043

基金项目:

国家“973”计划

详细信息

作者简介:
孙泽斌, 男, 博士研究生。主要研究方向:故障诊断、故障预测、图像处理

赵琦, 女, 博士, 副教授。主要研究方向:故障诊断、通信信号处理、认知无线电

冯文全, 男, 博士, 教授, 博士生导师。主要研究方向:故障诊断、通信信号处理、卫星导航信号处理

张文峰, 男, 博士研究生。主要研究方向:故障诊断、故障预测

通讯作者:
赵洪博, 男, 博士, 讲师。主要研究方向:故障诊断、卫星导航信号处理, E-mail:bhzhb@buaa.edu.cn

中图分类号: TB114.3
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- 被引次数: 0
出版历程
- 收稿日期: 2016-04-19
- 录用日期: 2016-04-29
- 网络出版日期: 2017-02-20

An SVR based hybrid modeling method

1.
School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2.
State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an 710043, China

Funds:

National Basic Research Program of China

More Information

Corresponding author: ZHAO Hongbo, E-mail:bhzhb@buaa.edu.cn

摘要

摘要:
近年来，随着计算能力的不断提高，数据驱动的建模方法受到了广泛的关注，对单模式系统进行定量分析的建模方法获得了诸多研究。然而，实际应用中大多数系统为多模式系统，不但各个模式有着不同的连续行为，连续状态还会在模式之间进行切换。针对这一情形，本文提出了经验概率混合自动机模型，并提出了针对该模型的基于支持向量回归（SVR）的多模式定性定量混合建模方法。该方法使用小波技术识别模式切换点，并在各个模式下单独建立支持向量模型，最后使用D-Markov机整合模型。经实例验证，该方法与传统支持向量回归模型的稳定性接近，但精确程度显著提高。
- 混合建模 /
- 支持向量回归(SVR) /
- D-Markov机 /
- 小波 /
- 数据驱动的建模
Abstract:
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.
- hybrid modeling /
- support vector regression (SVR) /
- D-Markov machine /
- wavelet /
- data-driven modeling

HTML全文

图 1 慢时间尺度与快时间尺度

Figure 1. Slow time scale and fast time scale

下载: 全尺寸图片幻灯片

图 2 混合建模框图

Figure 2. Block diagram of hybrid modeling

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图 3 混合系统行为

Figure 3. Behavior of hybrid system

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图 4 模式切换点识别

Figure 4. Recognition of mode switching points

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图 5 EPHA模型与SVR模型系统行为曲线

Figure 5. System behavior curves of EPHA model and SVR model

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表 1 SVR与EPHA均方误差

Table 1. Mean square error of SVR and EPHA

序号	均方误差
序号	SVR	EPHA
1	0.089 6	0.071 6
2	0.094 5	0.067 9
3	0.094 4	0.072 8
4	0.091 6	0.073 3
5	0.093 5	0.071 8
6	0.089 6	0.072 2
7	0.091 2	0.070 1
8	0.094 2	0.071 6
9	0.093 5	0.068 7
10	0.094 5	0.072 2
方差	3.95×10^-6	3.11×10^-6
均值	0.092 7	0.071 2

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