The recursive least squares identification algorithm for a class of Wiener nonlinear systems

Journal of the Franklin Institute

Volumn 353, Issue 7, 2016, Pages 1518-1526

  Ding, Feng ^a   Liu, Ximei ^a   Liu, Manman ^a  

^a QINGDAO UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR CONTROL SYSTEMS; NONLINEAR SYSTEMS;

AUXILIARY MODEL IDENTIFICATION IDEAS; INFORMATION VECTOR; LINEAR DYNAMIC SYSTEM; NONLINEAR FUNCTIONS; NONLINEAR STATIC FUNCTION; PARAMETER ESTIMATE; RECURSIVE LEAST SQUARES ALGORITHMS; RECURSIVE LEAST-SQUARES IDENTIFICATIONS;

ALGORITHMS;

EID: 84959564700     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2016.02.013     Document Type: Article

References (40)

1. Y. Shi, and H. Fang Kalman filter based identification for systems with randomly missing measurements in a network environment Int. J. Control 83 3 2010 538 551
2. Y. Shi, and B. Yu Output feedback stabilization of networked control systems with random delays modeled by Markov chains IEEE Trans. Autom. Control 54 7 2009 1668 1674
3. Y. Shi, and B. Yu Robust mixed H-2/H-infinity control of networked control systems with random time delays in both forward and backward communication links Automatica 47 4 2011 754 760
4. F. Ding System Identification - New Theory and Methods 2013 Science Press Beijing
5. F. Ding System Identification - Performances Analysis for Identification Methods 2014 Science Press Beijing
6. P.P. Hu, F. Ding, and J. Sheng Auxiliary model based least squares parameter estimation algorithm for feedback nonlinear systems using the hierarchical identification principle J. Frankl. Inst. - Eng. Appl. Math. 350 10 2013 3248 3259
7. X.L. Luan, P. Shi, and F. Liu Stabilization of networked control systems with random delays IEEE Trans. Ind. Electron. 58 9 2011 4323 4330
8. X.L. Luan, S.Y. Zhao, and F. Liu H-infinity control for discrete-time Markov jump systems with uncertain transition probabilities IEEE Trans. Autom. Control 58 6 2013 1566 1572
9. P. Shi, X.L. Luan, and F. Liu H-infinity filtering for discrete-time systems with stochastic incomplete measurement and mixed delays IEEE Trans. Ind. Electron. 59 6 2012 2732 2739
10. F. Ding, X.H. Wang, Q.J. Chen, and Y.S. Xiao Recursive least squares parameter estimation for a class of output nonlinear systems based on the model decomposition Circuits Syst. Signal Process. 35 2016 10.1007/s00034-015-0190-6
11. D.Q. Wang Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models Appl. Math. Lett. 57 2016 13 19
12. H. Li, Y. Gao, P. Shi, and H.K. Lam Observer-based fault detection for nonlinear systems with sensor fault and limited communication capacity IEEE Trans. Autom. Control 2016 10.1109/TAC.2015.2503566
13. H. Li, P. Shi, D. Yao, and L. Wu Observer-based adaptive sliding mode control of nonlinear Markovian jump systems Automatica 64 2016 133 142
14. Y.J. Wang, and F. Ding Iterative estimation for a nonlinear IIR filter with moving average noise by means of the data filtering technique IMA J. Math. Control Inf. 2016 10.1093/imamci/dnv067
15. J.H. Li Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration Appl. Math. Lett. 26 1 2013 91 96
16. G. Shafiee, M.M. Arefi, M.R. Jahed-Motlagh, and A.A. Jalali Nonlinear predictive control of a polymerization reactor based on piecewise linear Wiener model Chem. Eng. J. 143 1-3 2008 282 292
17. X. Jin, B. Huang, and D.S. Shook Multiple model LPV approach to nonlinear process identification with EM algorithm J. Process Control 21 1 2011 182 193
18. M.M. da Silva, T. Wigren, and T. Mendon Nonlinear identification of a minimal neuromuscular blockade model in anesthesia IEEE Trans. Control Syst. Technol. 20 1 2012 181 188
19. K. Pelckmans MINLIP for the identification of monotone Wiener systems Automatica 47 10 2011 2298 2305
20. W.L. Xiong, J.X. Ma, and R.F. Ding An iterative numerical algorithm for modeling a class of Wiener nonlinear systems Appl. Math. Lett. 26 4 2013 487 493
21. J. Vörös Parameter identification of Wiener systems with multisegment piecewise-linear nonlinearities Syst. Control Lett. 56 2 2007 99 105
22. A. Hagenblad, L. Ljung, and A. Wills Maximum likelihood identification of Wiener models Automatica 44 11 2008 2697 2705
23. A. Hasanov, and V.G. Romanov An inverse coefficient problem related to elastic-plastic torsion of a circular cross-section bar Appl. Math. Lett. 26 5 2013 533 538
24. E.W. Bai A blind approach to the Hammerstein-Wiener model identification Automatica 38 6 2002 967 979
25. S.L. Lacy, and D.S. Berntein Identification of FIR Wiener systems with unknown, non-invertible, polynomial non-linearities Int J. Control 76 15 2003 1500 1507
26. A. Janczak Identification of Nonlinear Systems Using Neural Networks and Polynomial Models 2005 Springer Berlin, Heidelberg
27. M.S. Revathy, and N.N. Singh An efficient way of solving inverse problem using nonlinear Wiener filter and its application to pattern recognition Procedia Eng. 38 2012 708 717
28. I.A. Krishtal, and K.A. Okoudjou Invertibility of the Gabor frame operator on the Wiener amalgam space J. Approx. Theory 153 2 2008 212 224
29. Y.B. Hu, B.L. Liu, Q. Zhou, and C. Yang Recursive extended least squares parameter estimation for Wiener nonlinear systems with moving average noises Circuits Syst. Signal Process. 33 2 2014 655 664
30. Y.B. Hu, B.L. Liu, and Q. Zhou A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems Appl. Math. Comput. 247 2014 218 224
31. Y. Ji, X.M. Liu, and et al. New criteria for the robust impulsive synchronization of uncertain chaotic delayed nonlinear systems Nonlinear Dyn. 79 1 2015 1 9
32. Y. Ji, and X.M. Liu Unified synchronization criteria for hybrid switching-impulsive dynamical networks Circuits Syst. Signal Process. 34 5 2015 1499 1517
33. J. Ding, C.X. Fan, and J.X. Lin Auxiliary model based parameter estimation for dual-rate output error systems with colored noise Appl. Math. Modell. 37 6 2013 4051 4058
34. F. Ding, X.M. Liu, and Y. Gu An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering J. Frankl. Inst. 353 2 2016 398 408
35. J. Ding, and J.X. Lin Modified subspace identification for periodically non-uniformly sampled systems by using the lifting technique Circuits Syst. Signal Process. 33 5 2014 1439 1449
36. X.H. Wang, and F. Ding Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems Int. J. Adapt. Control Signal Process 30 2016 10.1002/acs.2642
37. F. Ding, Y.J. Wang, and J. Ding Recursive least squares parameter identification for systems with colored noise using the filtering technique and the auxiliary model Digital Signal Process 37 2015 100 108
38. X.H. Wang, and F. Ding Modelling and multi-innovation parameter identification for Hammerstein nonlinear state space systems using the filtering technique Math. Comput. Model. Dyn. Syst. 22 2016 http://dx.doi.org/10.1080/13873954.2016.1142455
39. Y.J. Wang, and F. Ding Recursive least squares algorithm and gradient algorithm for Hammerstein-Wiener systems using the data filtering Nonlinear Dyn 2016 10.1007/s11071-015-2548-5
40. Y.J. Wang, and F. Ding Recursive parameter estimation algorithms and convergence for a class of nonlinear systems with colored noise Circuits Syst. Signal Process 35 2016 10.1007/s00034-015-0210-6