基于动态多核相关向量机的软测量建模研究

吴菁; 刘乙奇; 刘坚; 黄道平; 邱禹; 于广平

doi:10.11949/j.issn.0438-1157.20181240

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基于动态多核相关向量机的软测量建模研究

过程系统工程 | 更新时间：2026-07-24

- 基于动态多核相关向量机的软测量建模研究
- Study on the soft sensor of multi-kernel relevance vector machine based on time difference
- 化工学报 2019年70卷第4期页码：1472-1484
- 作者机构：
  
  1.华南理工大学自动化科学与工程学院，广东广州 510640
  2.浙江大学工业控制技术国家重点实验室，浙江杭州 310027
  3.贵州民族大学数据科学与信息工程学院, 贵州贵阳 550025
  4.广州中国科学院沈阳自动化研究所分所，广东广州 511458
- 作者简介：
  
  吴菁（1988—），女，博士研究生，讲师，ipicq@163.com
  黄道平（1961—），男，博士，教授，audhuang@scut.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61873096;61673181;61533002);广东省科技计划项目(2016A020221007);2017中央高校基本科研业务资助项目一面上项目(2017MS053);广州市科技计划项目(201804010256);工业控制技术国家重点实验室课题(ICT1800372);贵州省科技厅联合基金项目(黔科合LH字[2014]7379)
- DOI：10.11949/j.issn.0438-1157.20181240
  中图分类号： TP 277
- 收稿：2018-10-18，
  
  修回：2018-12-25，
  
  网络首发：2020-06-15，
  
  纸质出版：2019-04-12
- 稿件说明：
移动端阅览
吴菁, 刘乙奇, 刘坚, 黄道平, 邱禹，于广平. 基于动态多核相关向量机的软测量建模研究[J]. 化工学报, 2019, 70(4): 1472-1484

WU Jing, LIU Yiqi, LIU Jian, HUANG Daoping, QIU Yu, YU Guangping. Study on the soft sensor of multi-kernel relevance vector machine based on time difference[J]. CIESC Journal, 2019, 70(4): 1472-1484
吴菁, 刘乙奇, 刘坚, 黄道平, 邱禹，于广平. 基于动态多核相关向量机的软测量建模研究[J]. 化工学报, 2019, 70(4): 1472-1484 DOI： 10.11949/j.issn.0438-1157.20181240.

WU Jing, LIU Yiqi, LIU Jian, HUANG Daoping, QIU Yu, YU Guangping. Study on the soft sensor of multi-kernel relevance vector machine based on time difference[J]. CIESC Journal, 2019, 70(4): 1472-1484 DOI： 10.11949/j.issn.0438-1157.20181240.

摘要

针对污水处理过程中存在的多变量耦合、强非线性以及参数时变等问题，提出基于多核学习相关向量机的软测量建模方法，并采用粒子群算法对多核权重以及核参数进行优化。同时，引入时间差分(time difference)方法改进多核相关向量机的动态特性。为了验证所提模型的有效性，通过一仿真案例与单核相关向量机、多层前馈神经网络和基于遗传算法的支持向量机进行对比研究。结果表明，所提模型具有更好的预测效果。最后，对模型的鲁棒性在数据漂移和异常的场景下进行了讨论。

Abstract

Considering the characteristics of strong multivariable coupling

significant non-linearity and parameter time-varying in the wastewater treatment processes

a multi-kernel relevance vector machine (MRVM) is proposed for soft-sensor modeling. Particle swarm optimization algorithm is further used to optimize multi-kernel weights and kernel parameters. Meanwhile

the time difference (TD) method is introduced to improve the dynamic characteristics of MRVM. The proposed model was demonstrated through a WWTP simulated case study by comparison with relevance vector machine (RVM) with a single kernel

back propagation (BP) neural network and the genetic algorithm-based support vector machine (GA-SVM). Results showed that the proposed model achieved better prediction accuracy. Finally

the robustness of the models is discussed in the context of data drift and anomalies.

关键词

Keywords

references

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