Évf. 6 szám 1 (2019)

Articles

System Identification and Model Validation of Recursive Least Squares Algorithm for Box–Jenkins Systems

Megjelent:

2019-12-23

https://doi.org/10.17667/riim.2019.1/4.

Szerzők

Aldian Ambark Shashoa Nasar,

Electrical and Electronics Engineering Department, Azzaytuna University

S. Elmezughi Abdurrezag,

Computer Engineering Department, Azzaytuna University

N. Jleta Ibrahim,

Department of Electrical Engineering, Libyan Academy of Graduate Studies

B. Ekreem Nasser

Computer Engineering Department, Azzaytuna University

Megtekintés

Pdf (Angol)

Kulcsszavak

system identification model validation parameter estimation histogram Box–Jenkins system

Licenc

Copyright (c) 2019 by the authors

This work is licensed under a Creative Commons Attribution 4.0 International License.

Hogyan hivatkozzuk

Kiválasztott formátum: APA

Aldian Ambark Shashoa, N., S. Elmezughi, A., N. Jleta, I., & B. Ekreem, N. (2019). System Identification and Model Validation of Recursive Least Squares Algorithm for Box–Jenkins Systems. Recent Innovations in Mechatronics, 6(1), 1-6. https://doi.org/10.17667/riim.2019.1/4.

Hivatkozási formátumok

Hivatkozások
• ACM
• ACS
• APA
• ABNT
• Chicago
• Harvard
• IEEE
• MLA
• Turabian
• Vancouver
• AMA

---

Letölthető hivatkozások
Endnote/Zotero/Mendeley (RIS) BibTeX

Absztrakt

In this paper, a new-type recursive least squares algorithm is proposed for identifying the system model parameters and the noise model parameters of Box–Jenkins Systems. The basic idea is based on replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. Validation of the model is evaluated using some statistical methods, Which, best-fit criterion and Histogram. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

Hivatkozások

1. Hernan Guidi, “Open and Closed-loop Model Identification and Validation”, Master Thesis, University of Pretoria December 2008.
2. F. Chen and F. Ding, “Recursive Least Squares Identification Algorithm for Multiple-Input Nonlinear Box-Jenkins Systems Using Maximum Likelihood Principle”, Journal of Computational and Nonlinear Dynamics Vol. 11, 2016.
3. Y. Gu, F. Ding, J. Li, “State filtering and parameter estimation for linear systems with d-step statedelay”, IET Signal Processing. Vol. 8, Iss. 6, pp. 639–646, 2016.
4. D. Meng and F. Ding, “Model Equivalence-Based Identification Algorithm for Equation-Error Systems with Colored Noise”, Algorithms. pp. 280-291, 2015.
5. Y. Xiao, Y. Zhang, J. Ding and J. Daic, “The residual based interactive least squares algorithms and simulation studies”, Computers and Mathematics with Applications. pp. 1190-1197, 2009.
6. S. Wang and R. Ding, “Three-stage recursive least squares parameter estimation for controlled autoregressive autoregressive systems”, Applied Mathematical Modelling. pp. 7489-7497, 2013.
7. H. Hu, X.Yongsong and R. Ding, “Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition,” Journal of Applied Mathematics, Vol. 2013, September 2013.
8. R. G. Sargent, “Verification and validation of simulation models”, Winter Simulation Conference, January 2011.
9. S. Gibson, A. Wills, and B. Ninness, “Maximum-likelihood parameter estimation of bilinear systems,” IEEE Trans. Autom. Control, vol. 50,no. 10, pp. 1581_1596, Oct. 2005.
10. M. Li, X. Liu, and F. Ding, “The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise,” J. Franklin Inst., vol. 354, no. 12, pp. 4861_4881, Aug. 2017.
11. D. Meng, “Recursive least squares and multi-innovation gradient estimation algorithms for bilinear stochastic systems”,Circuits Syst. Signal Process., vol. 36, no. 3, pp. 1052_1065, Mar. 2017.
12. M. Li, X. Liu, and F. Ding, “The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise”,Circuits Syst. Signal Process., vol. 36, no. 11, pp. 4541_4568, Nov. 2017.
13. M. Li, X. Liu, and F. Ding, “Least-squares-based iterative and gradientbased iterative estimation algorithms for bilinear systems”,Nonlinear Dyn., vol. 89, no. 1, pp. 197_211, Jul. 2017.
14. D. Westwick and M. Verhaegen, “Identifying MIMO Wiener systems using subspace model identi_cation methods”,Signal Process., vol. 52, no. 2, pp. 235_258, Jul. 1996.
15. V. Verdult and M. Verhaegen, “Subspace identi_cation of multivariable linear parameter-varying systems”,Automatica, vol. 38, no. 5, pp. 805_814, May 2002.
16. Y.-X. Zheng and Y. Liao, “Parameter identi_cation of nonlinear dynamic systems using an improved particle swarm optimization”,Optik-Int. J. Light Electron Opt., vol. 127, no. 19, pp. 7865_7874, Oct. 2016.
17. S.-H. Tsai and Y.-W. Chen, “A novel fuzzy identi_cation method based on ant colony optimization algorithm”,IEEE Access, vol. 4, pp.
18. _3756, 2016.
19. P. T. Brewick and S. F. Masri, “An evaluation of data-driven identi_cation strategies for complex nonlinear dynamic systems”,Nonlinear Dyn., vol. 85, no. 2, pp. 1297_1318, Jul. 2016.
20. W. Xiong, X. Yang, L. Ke, and B. Xu, “EM algorithm-based identi_cation of a class of nonlinear Wiener systems with missing output data”,Nonlin-ear Dyn., vol. 80, nos. 1_2, pp. 329_339, Apr. 2015.
21. M. Li, X. Liu, “ Auxiliary Model Based Least Squares Iterative Algorithms for Parameter Estimation of Bilinear Systems”, IEEE Access Vol. 6, 2018.
22. L.Xu, “The parameter estimation algorithms based on the dynamical response measurement data ”, Advances in Mechanical Engineering. Vol. 9, Iss. 11, pp. 1–12, 2017.
23. X. Lu, W. Zhou and W. Shi, “Data Filtering Based Recursive Least Squares Algorithm for Two-Input Single-Output Systems with Moving Average Noises”, Journal of Applied Mathematics., vol. 2014, 2014.
24. F. Ding and T. Chen, “Parameter estimation of dual-rate stochastic systems by using an output error method,” IEEE Transactions on Automatic Control, vol. 50, no. 9, pp. 1436–1441,2005.
25. F. Ding and J. Ding, “Least-squares parameter estimation for systems with irregularly missing data,” International Journal of Adaptive Control and Signal Processing, vol. 24, no. 7, pp. 540–553,2010.
26. S. Rachad, B. Nsiri and B.l Bensassi, “System identification of inventory system using ARX and ARMAX models,” International Journal of control and automation, December 2015.
27. L. Ljung, “System Identification Theory For User”, Prentice Hall Ptr, 1999.
28. K. Burnham, D. Anderson, “Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach,” Springer, 2002.
29. G.Yaoa and R. Ding, “Two-stage least squares based iterative identification algorithm for controlled autoregressive moving average
30. (CARMA) systems”, Computers and Mathematics with Applications. Vol. 8, Iss. 6, pp. 975–984, 2012.
31. H. Shariff, M. Rahiman, I. Yassin And M. Tajjudin, “System Identification Of A Steam Distillation Pilotscale Using Arx And Narx Approaches,” International Journal Of,Reasearch And Tecnology, Vol. 03, Issue:01, 2014.