Advertisement

Simultaneous identification of process structure, parameters and time-delay based on non-negative garrote

  • Jian-Guo Wang
  • Qian-Ping XiaoEmail author
  • Tiao Shen
  • Shi-Wei Ma
  • Wen-Tao Rao
  • Yong-Jie Zhang
Research Article

Abstract

In practice, the model structure, parameters and time-delay of the actual process may vary simultaneously. However, the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application. In view of the fact that variable selection procedure can ensure a compact model with robust input-output relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure, parameters and time-delay, non-negative garrote (NNG) algorithm is introduced and applied to system identification and the corresponding procedures are presented. The application of NNG variable selection algorithm to the identification of single input single output (SISO) system, multiple input multiple output (MIMO) system and Wood-Berry tower industry are investigated. The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square (OLS) algorithms. The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure, parameters and time-delay with high precision.

Keywords

Model structure model parameter time-delay non-negative garrote variable selection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    L. Fortuna, S. Graziani, A. Rizzo. Soft Sensors for Monitoring and Control of Industrial Processes, Berlin, Germary: Springer-Verlag, 2007.zbMATHGoogle Scholar
  2. [2]
    Y. P. Badhe, J. Lonari, S. S. Tambe, B. D. Kulkarni, N. K. Valecha, S. V. Deshmukh, S. Ravichandran. Improve polyethylene process control and product quality. Hydrocarbon Processing, vol. 86, no. 3, pp. 53–60, 2007.Google Scholar
  3. [3]
    K. Desai, Y. Badhe, S. S. Tambe, B. D. Kulkarni. Softsensor development for fed-batch bioreactors using support vector regression. Biochemical Engineering Journal, vol. 27, no. 3, pp. 225–239, 2006.CrossRefGoogle Scholar
  4. [4]
    K. Sun, J. L. Liu, J. L. Kang, S. S. Jang, D. S. H. Wong, D. S. Chen. Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote. Journal of Process Control, vol. 24, no. 7, pp. 1068–1075, 2014.CrossRefGoogle Scholar
  5. [5]
    T. H. Pan, D. S. H. Wong, S. S. Jang. Development of a novel soft sensor using a local model network with an adaptive subtractive clustering approach. Industrial and Engineering Chemistry Research, vol. 49, no. 10, pp. 4738–4747, 2010.CrossRefGoogle Scholar
  6. [6]
    C. Y. Li, W. G. Li. Partial least squares method based on least absolute shrinkage and selection operator. In Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), IEEE, Chengdu, China, vol. 4, pp. 591–593, 2010.Google Scholar
  7. [7]
    R. J. Shi, J. F. MacGregor. Modeling of dynamic systems using latent variable and subspace methods. Journal of Chemometrics, vol. 14, no. 5–6, pp. 423–439, 2000.CrossRefGoogle Scholar
  8. [8]
    S. J. Qin. Recursive PLS algorithms for adaptive data modeling. Computers & Chemical Engineering, vol. 22, no. 4–5, pp. 503–514, 1998.CrossRefGoogle Scholar
  9. [9]
    S. Bhartiya, J. R. Whiteley. Development of inferential measurements using neural networks. ISA Transactions, vol. 40, no. 4, pp. 307–323, 2001.Google Scholar
  10. [10]
    L. Breiman. Better subset regression using the nonnegative garrote. Technometrics, vol. 37, no. 4, pp. 373–384, 1995.CrossRefzbMATHMathSciNetGoogle Scholar
  11. [11]
    R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodological), vol. 58, no. 1, pp. 267–288, 1996.zbMATHMathSciNetGoogle Scholar
  12. [12]
    H. Chun, S. Keles. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 72, no. 1, pp. 3–25, 2010.CrossRefMathSciNetGoogle Scholar
  13. [13]
    M. Yuan, Y. Lin. On the non-negative garrotte estimator. Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 69, no. 2, pp. 143–161, 2007.CrossRefzbMATHMathSciNetGoogle Scholar
  14. [14]
    J. Mohieddine. An overview of control performance assessment technology and industrial application. Control Engineering Practice, vol. 14, no. 5, pp. 441–466, 2006.CrossRefGoogle Scholar
  15. [15]
    A. Alenany, H. Shang, M. Soliman, I. Ziedan. Improved subspace identification with prior information using constrained least squares. Control Theory & Applications, vol. 5, no. 13, pp. 1568–1576, 2010.CrossRefMathSciNetGoogle Scholar
  16. [16]
    C. C. Pan, J. Bai, G. G. Yang, D. S. H. Wong, S. S. Jang. An inferential modeling method using enumerative PLS based nonnegative garrote regression. Journal of Process Control, vol. 22, no. 9, pp. 1637–1646, 2012.CrossRefGoogle Scholar
  17. [17]
    H. Chun, S. Keles. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 72, no. 1, pp. 3–25, 2010.CrossRefMathSciNetGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jian-Guo Wang
    • 1
  • Qian-Ping Xiao
    • 1
    Email author
  • Tiao Shen
    • 1
  • Shi-Wei Ma
    • 1
  • Wen-Tao Rao
    • 2
  • Yong-Jie Zhang
    • 2
  1. 1.School of Mechatronical Engineering and AutomationShanghai University, Shanghai Key Lab of Power Station Automation TechnologyShanghaiChina
  2. 2.Institute of Environment and ResourcesBaosteel Research InstituteShanghaiChina

Personalised recommendations