A variational Bayesian method to robust identification based on over-sampling structure

XU Baochang; BAI Zhenxuan; WANG Yaxin; YUAN Likun

doi:10.11949/0438-1157.20190880

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A variational Bayesian method to robust identification based on over-sampling structure

Process system engineering | 更新时间：2026-07-24

- A variational Bayesian method to robust identification based on over-sampling structure
- CIESC Journal Vol. 70, Issue 12, Pages: 4673-4679(2019)
- 作者机构：
  
  中国石油大学(北京)自动化系，北京 102249
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20190880
  CLC： TP 273
- Received：05 August 2019，
  
  Revised：2019-08-15，
  
  Online First：09 June 2020，
  
  Published：05 December 2019
- 稿件说明：
移动端阅览
徐宝昌, 白振轩, 王雅欣, 袁力坤. 基于过采样结构的贝叶斯鲁棒辨识方法[J]. 化工学报, 2019, 70(12): 4673-4679

XU Baochang, BAI Zhenxuan, WANG Yaxin, YUAN Likun. A variational Bayesian method to robust identification based on over-sampling structure[J]. CIESC Journal, 2019, 70(12): 4673-4679
徐宝昌, 白振轩, 王雅欣, 袁力坤. 基于过采样结构的贝叶斯鲁棒辨识方法[J]. 化工学报, 2019, 70(12): 4673-4679 DOI： 10.11949/0438-1157.20190880.

XU Baochang, BAI Zhenxuan, WANG Yaxin, YUAN Likun. A variational Bayesian method to robust identification based on over-sampling structure[J]. CIESC Journal, 2019, 70(12): 4673-4679 DOI： 10.11949/0438-1157.20190880.

摘要

在实际工业过程中，异常值的干扰是不可避免的，现有的处理异常值方法会导致模型估计有偏差，并且没有考虑潜在异常值的影响。针对上述缺点，利用学生分布噪声来处理潜在异常值，提出一种适用于学生分布噪声情况的贝叶斯鲁棒辨识方法，并且将其与过采样结构相结合，推出了基于过采样结构的贝叶斯鲁棒辨识方法。仿真实验表明：本文提出的算法，随着异常值影响的增加，仍然保持较小的辨识误差，而传统辨识方法已不再适用，同时，还克服了传统结构需添加额外测试信号所带来的巨额成本。因此，本文的算法更适合于实际工业过程辨识。

Abstract

In the actual industrial process

the interference of outliers is inevitable. Existing methods of dealing with outliers can cause bias in model estimates and do not take into account the effects of potential outliers. In view of the above shortcomings

using student distributed noise to deal with potential outliers

this paper proposes a variational Bayesian method based on student distribution noise

and combines it with over-sampling structure to introduce a variational Bayesian method to robust identification based on over-sampling structure. The simulation experiments show when the outlier has a large influence

it still maintains a small identification error

while the traditional identification method is no longer applicable

and it also overcomes the huge cost of adding additional test signals to the traditional structure. Therefore

the algorithm in this paper is more suitable for practical industrial process identification.

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references

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