Bias compensation-based parameter estimation for output error moving average systems

International Journal of Adaptive Control and Signal Processing

Volumn 25, Issue 12, 2011, Pages 1100-1111

  Ding, Jie ^a,b   Ding, Feng ^b  

^a NANJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b JIANGNAN UNIVERSITY   (China)

Author keywords

bias compensation; hierarchical identification; least squares; output error systems; recursive identification

Indexed keywords

BIAS COMPENSATION; HIERARCHICAL IDENTIFICATION; LEAST SQUARE; OUTPUT ERROR SYSTEMS; RECURSIVE IDENTIFICATION;

ALGORITHMS; ERROR COMPENSATION; ESTIMATION; HIERARCHICAL SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS);

PARAMETER ESTIMATION;

EID: 80755182664     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.1266     Document Type: Article

References (27)

1. Lindoff B, Holst J,. Convergence analysis of the RLS identification algorithm with exponential forgetting in stationary ARX-structures. International Journal of Adaptive Control and Signal Processing 1999; 13 (1): 1-22.
2. Söderström T, Hong M, Zheng WX,. Convergence properties of bias-eliminating algorithms for errors-in-variables identification. International Journal of Adaptive Control and Signal Processing 2005; 19 (9): 703-722. (Pubitemid 41596124)
3. Zhu Y, Telkamp H, Wang J, Fu Q,. System identification using slow and irregular output samples. Journal of Process Control 2009; 19 (1): 58-67.
4. Ding F, Chen T, Qiu L,. Bias compensation based recursive least squares identification algorithm for MISO systems. IEEE Transactions on Circuits and Systems-II: Express Briefs 2006; 53 (5): 349-353. (Pubitemid 43786576)
5. Ding F, Ding J,. Least-squares parameter estimation for systems with irregularly missing data. International Journal of Adaptive Control and Signal Processing 2010; 24 (7): 540-553.
6. Zhang Y, Cui GM,. Bias compensation methods for stochastic systems with colored noise. Applied Mathematical Modelling 2011; 35 (4): 1709-1716.
7. Wigren T,. Recursive prediction error identification and scaling of nonlinear state space models using a restricted black box parametrization. Automatica 2006; 42 (1): 159-168. (Pubitemid 41660573)
8. Zheng WX, Feng C-B,. A bias-correction method for indirect identification of closed-loop systems. Automatica 1995; 31 (7): 1019-1024.
9. Mahata K,. An improved bias-compensation approach for errors-in-variables model identification. Automatica 2007; 43 (8): 1339-1354. (Pubitemid 47016365)
10. Gilson M, Van den Hof P,. On the relation between a bias-eliminated least square (BELS) and an IV estimator in closed-loop identification. Automatica 2001; 37 (10): 1593-1600. (Pubitemid 32737015)
11. Söderström T, Soverini U, Mahata K,. Perspectives on errors-in-variables estimation for dynamic systems. Signal Processing 2002; 82 (8): 1139-1154. (Pubitemid 34660648)
12. Yang HZ, Zhang Y,. Comparisons of bias compensation methods and other identification approaches for Box-Jenkins models. Control Theory and Applications 2007; 24 (2): 215-222. (Pubitemid 46859210)
13. Zheng WX,. On a least-squares-based algorithm for identification of stochastic linear systems. IEEE Transactions on Signal Processing 1998; 46 (6): 1631-1638. (Pubitemid 128743932)
14. Zheng WX,. Transfer function estimation from noisy input and output data. International Journal of Adaptive Control and Signal Processing 1998; 12 (4): 365-380. (Pubitemid 128552839)
15. Zheng WX,. A bias correction method for identification of linear dynamic errors-in-variables models. IEEE Transactions on Automatic Control 2002; 47 (7): 1142-1147. (Pubitemid 34835341)
16. Zhang Y, Yang HZ,. Bias compensation recursive least squares identification for output error systems with colored noises. Acta Automatica Sinica 2007; 33 (10): 1053-1060. (Pubitemid 350062619)
17. Söderström T,. Extending the Frisch scheme for errors-in-variables identification to correlated output noise. International Journal of Adaptive Control and Signal Processing 2008; 22 (1): 55-73. (Pubitemid 351315657)
18. Goodwin GC, Sin KS,. Adaptive Filtering Prediction and Control. Prentice-Hall: Englewood Cliffs, NJ, 1984.
19. Ding J, Ding F,. The residual based extended least squares identification method for dual-rate systems. Computers & Mathematics with Applications 2008; 56 (6): 1479-1487.
20. Ljung L,. System Identification: Theory for the User, 2nd ed. Prentice-Hall: Englewood Cliffs, NJ, 1999.
21. Ding J, Shi Y, Wang H, Ding F,. A modified stochastic gradient based parameter estimation algorithm for dual-rate sampled-data systems. Digital Signal Processing 2010; 20 (4): 1238-1247.
22. Ding F, Qiu L, Chen T,. Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems. Automatica 2009; 45 (2): 324-332.
23. Ding F, Chen T,. Performance analysis of multi-innovation gradient type identification methods. Automatica 2007; 43 (1): 1-14. (Pubitemid 44792035)
24. Ding F,. Several multi-innovation identification methods. Digital Signal Processing 2010; 20 (4): 1027-1039.
25. Han LL, Ding F,. Multi-innovation stochastic gradient algorithms for multi-input multi-output systems. Digital Signal Processing 2009; 19 (4): 545-554.
26. Zhang JB, Ding F, Shi Y,. Self-tuning control based on multi-innovation stochastic gradient parameter estimation. Systems & Control Letters 2009; 58 (1): 69-75.
27. Liu YJ, Xiao YS, Zhao XL,. Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model. Applied Mathematics and Computation 2009; 215 (4): 1477-1483.