CIESC Journal ›› 2019, Vol. 70 ›› Issue (4): 1472-1484.DOI: 10.11949/j.issn.0438-1157.20181240

• Process system engineering • Previous Articles     Next Articles

Study on the soft sensor of multi-kernel relevance vector machine based on time difference

Jing WU1,3(),Yiqi LIU1,2,Jian LIU4,Daoping HUANG1(),Yu QIU1,Guangping YU4   

  1. 1. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
    2. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China
    3. School of Information Engineering, Guizhou Minzu University, Guiyang 550025, Guizhou, China
    4. Shenyang Institute of Automation, Chinese Academy of Sciences, Guangzhou 511458, Guangdong, China
  • Received:2018-10-18 Revised:2018-12-25 Online:2019-04-05 Published:2019-04-05
  • Contact: Daoping HUANG

基于动态多核相关向量机的软测量建模研究

吴菁1,3(),刘乙奇1,2,刘坚4,黄道平1(),邱禹1,于广平4   

  1. 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
  • 通讯作者: 黄道平
  • 作者简介:<named-content content-type="corresp-name">吴菁</named-content>(1988—),女,博士研究生,讲师,<email>ipicq@163.com</email>|黄道平(1961—),男,博士,教授,<email>audhuang@scut.edu.cn</email>
  • 基金资助:
    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)

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.

Key words: soft sensor, wastewater treatment, multi-kernel, relevance vector machine, time difference

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。

关键词: 软测量, 污水处理, 多核, 相关向量机, 时差建模

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