CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Novel Developed Linear and Nonlinear System Identification for an Industrial Dryer

by Mostafa Darvishi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 10
Year of Publication: 2013
Authors: Mostafa Darvishi
10.5120/12921-9908

Mostafa Darvishi . A Novel Developed Linear and Nonlinear System Identification for an Industrial Dryer. International Journal of Computer Applications. 74, 10 ( July 2013), 27-35. DOI=10.5120/12921-9908

@article{ 10.5120/12921-9908,
author = { Mostafa Darvishi },
title = { A Novel Developed Linear and Nonlinear System Identification for an Industrial Dryer },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 10 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 27-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number10/12921-9908/ },
doi = { 10.5120/12921-9908 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:53.515563+05:30
%A Mostafa Darvishi
%T A Novel Developed Linear and Nonlinear System Identification for an Industrial Dryer
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 10
%P 27-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Dryer system is a controlled plant which is normally used in chemical industry. The proper modeling of this system facilitates its maintenance and keeping. Normally this is not an easy task, unless having a complete model of the process, due to sudden and nonlinear events. In this paper the linear identification of dryer plant in Arak petroleum, is introduced according to the Autoregressive with Exogenous Input (ARX) model and Box-Jenkins model and also the nonlinear identification based on Multi-Layer Perceptron (MLP) algorithm is investigated. The simulation results were satisfactory. It was concluded that these models can be used to design adaptive or robust controllers.

References
  1. Nelles, O. , 2001, Nonlinear System Identification-From Classical Approaches to Neural Networks and Fuzzy Model, Springer-Vetlag, Berlin Heidelberg, New York.
  2. Nelles, O. , Fink, A. and Isermann, 2000, Local Linear Model Trees (LOLIMAT), toolbox for nonlinear system identification, IFAC.
  3. Billings, S. A. and Fakhouri, S. Y. , 1982, Identification of systems containing linear dynamic and static nonlinear elements, Automatica, pp. 15-26.
  4. Oliveira, G. H. C. , Da Rosa, A. , Campello, J. G. B. R. , Machado, B. J. , and Amaral, W. C. , 2012, An introduction to models based on Laguerre, Kautz and other related orthonormal functions – Part II: non-linear models, Int. J. of Modelling, Identification and Control (IJMIC), Vol. 16, No. 1, pp. 1 – 14.
  5. Syariful S. , Sh. , and XiaoQi, Ch. , 2012, Identification of an unmanned helicopter system using optimised neural network structure, Int. J. of Modelling, Identification and Control (IJMIC), Vol. 17, No. 3, pp. 223 – 241.
  6. Wei, Ch. , and Junfei, Q. , 2012, Nonlinear system identification and fault detection method using RBF neural networks with set membership estimation, Int. J. of Modelling, Identification and Control (IJMIC).
  7. Ben-Zvi , A. , 17-19 July 2010, Modeling and parameter estimation for complex systems, 2nd International conference of Modelling, Identification and Control (ICMIC), Okayama, Japan.
  8. Stoyan, V. A. , 2003, Identification Problem for Matrix-Integrating Systems, Cybernetics and Systems Analysis, Volume 39, Number 5, pp. 753-764(12).
  9. Dayal, B. S. , and MacGregor, J. F. , 1997, Multi-output process identification, Journal of Process Control, Volume 7, Number 4, pp. 269-282(14).
  10. Taarit, I. , Belkoura, K. , Lotfi, K. , Mekki, R. , 2011, A fast identification algorithm for systems with delayed inputs, International Journal of Systems Science, Volume 42, Number 3, pp. 449-456(8).
  11. Alfi, A. , Fateh, M. , 2011, Identification of nonlinear systems using modified particle swarm optimisation: a hydraulic suspension system, Vehicle System Dynamics, Volume 49, Number 6, pp. 871-887(17).
  12. Rudenko, O. G. , Bessonov, A. A. , 2003, Real Time Identification of Nonlinear Time Varying Systems Using Radial Basis Function Network, Cybernetics and Systems Analysis, Volume 39, Number 6, pp. 927-934(8).
  13. Ljung, L. , 1999, System Identification, John Wiley & Sons.
  14. Narendra, K. S. , and Parthasarathy, K. , 1990, Identification and Control of Dynamical Systems Using Neural Networks, IEEE Transactions on Neural Networks, Vol. 1, No. 1.
  15. Sjoberg, J. , Zhang, Q. , Ljung, L. , Benveniste, A. , Delyon, B. , Glorennec, P. Y. , Hjalmarsson, H. , and Juditsky, A. , 1995, Nonlinear black-box modeling in system identification: a unified overview, Automatica, Vol. 31, No. 12, pp. 1691-1724.
  16. Billings, S. A. , and Fakhouri, S. Y. , 1982, Identification of systems containing linear dynamic and static nonlinear elements, Automatica, Vol. 18, No. 1, pp. 15-26.
  17. Emara-Shabaik, H. E. , 1996, Nonlinear systems modeling & identification using higher order statistics/polyspectra, Control and Dynamic Systems, Vol. 76, pp. 289-322.
  18. Bongard, J. C. , and Lipson, H. , 2005, Nonlinear System Identification Using Coevolution of Models and Tests, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 4.
  19. Juang, J-G, 2005, Nonlinear System Identification by Evolutionary Computation and Recursive Estimation Method, American Control Conference, Portland, OR, USA.
  20. Aleksander, I. , and Moton, H. , 1995, An Introduction to Neural Computing, Internat. Thomson Computer Press.
  21. Ash, T. , and Cottrell, G. , 1995, Topology-modifying neural network algorithms, The Handbook of Brain Theory and Neural Networks, Michael A. Arbib (ed. ), MIT Press, Cambridge MA.
  22. Baldi, P. , and Hornik, K. , 1989, Neural Networks and Principal Components Analysis: Learning from examples without local minima, Neural Networks, Vol. 2, No. 53.
  23. Hagan, M. T. , Demuth, H. B. , and Beale, M. H. , 2002, Neural Network Design, Vol. 10, Campus Publishing Service, Colorado University Bookstone.
  24. Rosenblatt, F. , Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan Press, Washington, DC, 1961.
  25. Riedmiller, M. , and Braun, H. , 1993, A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm, Proceedings of the IEEE International Conference on Neural Networks.
  26. Vogl, T. P. , Mangis, J. K, Rigler, A. K. , Zink, W. T. , and Alkon, D. L. 1988, Accelerating the convergence of the backpropagation method, Biological Cybernetics, Vol. 59, pp. 256-264.
  27. Hagan, M. T. , and Menhaj, M. , 1994, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, Vol. 5, No. 6, pp. 989–993.
  28. Kohonen, T. , 1988, Self-organization and Associative Memory, 2nd Edition, Berlin: Springer-Verlag.
  29. Kohonen, T. , 2001, Self-Organizing Maps, Springer.
  30. Ljung, L. , 1998, Nonlinear System Identification: Theory for the User, Pearson Education.
  31. Eykhoff, P. , 1974, System Identification: parameter and state estimation, Wiley-Interscience, Mathematics.
  32. Wang, L. , and Garnier, H. , 2012, System Identification, Environmetric Modeling and Control, Springer.
  33. Tulleken, H. J. A. F. , Generalized binary noise test-signal concept for improved identification-experiment design, Automatica, vol. 26, No. 1, pp. 34-49, 1990.
  34. Juang, J-N. , 1994, Applied System Identification, Prentice-Hall, Inc. Upper Saddle River, NJ, USA.
  35. Billings S. A, and Voon W. S. F. , 1986, Correlation based validity tests for nonlinear models, International journal of Control, Vol. 44, No. 1, pp. 235-244.
  36. Billings S. A, and Voon W. S. F. , 1987, Piecewise linear identification of non-linear systems, International journal of Control, Vol. 46, No. 1, pp. 215-235.
Index Terms

Computer Science
Information Sciences

Keywords

Dryer Linear system identification Nonlinear system identification ARX structure Box-Jenkins model

Powered by PhDFocusTM