石油化工高等学校学报

石油化工高等学校学报 ›› 2008, Vol. 21 ›› Issue (4): 95-98.

• 计算机与控制 • 上一篇    

广义支持向量机观测器的设计与仿真

彭红星1,2 , 陈祥光1 , 王红旗2   

  1. (1 .北京理工大学化工与环境学院, 北京100081 ; 2 .河南理工大学计算机科学与技术学院, 河南焦作454159)
  • 收稿日期:2008-03-07 出版日期:2008-12-20 发布日期:2017-07-05
  • 作者简介:彭红星(1977 -), 男, 河南焦作市, 在读博士
  • 基金资助:
    河南省重点科技攻关项目(072102240026);北京理工大学国际合作项目(BIT -UL 20070541002)

Design and Simulation of Support Vector Machines Generalized Observer

PENG Hong-xing1,2, CHEN Xiang-guang1, WANG Hong-qi2   

  1. (1. School of Chemical Engineering & Environment,Beijing Institute of Technology,Beijing 100081,P.R.China; 2. School of Computer Science & Technology,Henan Polytechnic University,Jiaozuo Henan 454159,P.R.China)
  • Received:2008-03-07 Published:2008-12-20 Online:2017-07-05

摘要:

提出了广义支持向量机观测器的概念并介绍了其设计方法。该观测器采用支持向量机回归算法拟
合过程变量之间的非线性关系, 由过程输入和除被观测输出之外的其它过程输出进行驱动, 可用于实现过程传感器
的故障检测和容错控制。多变量化工过程仿真实验表明, 广义支持向量机观测器克服了神经网络类方法在应用时
所存在的过学习、易陷入局部极小和结构选择困难等缺陷, 并且达到了很高的拟合精度。

关键词: 支持向量机 , 回归算法 , 广义观测器 , 神经网络

Abstract: Support vector machines generalized observer (SGO) was proposed and its design was introduced. The observer employs support vector machines (SVM) regression algorithm for fitting the nonlinearity among process variables. It was derived by process inputs and outputs except for the output to be monitored and can be used for process fault detection and fault tolerance control. multi-variables chemical process simulation results show that SVM overcomes some disadvantages of artificial neural network (ANN), such as over fitting, local minimization and difficulties in structure selection. SGO has a quite precise output in application.

Key words: Support vector machines , Regression algorithm , Generalized observer , Artificial neural network 

引用本文

彭红星, 陈祥光, 王红旗. 广义支持向量机观测器的设计与仿真[J]. 石油化工高等学校学报, 2008, 21(4): 95-98.

PENG Hong-xing, CHEN Xiang-guang, WANG Hong-qi. Design and Simulation of Support Vector Machines Generalized Observer[J]. Journal of Petrochemical Universities, 2008, 21(4): 95-98.

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