Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 226, Issue 1, 2012, Pages 43-55

  Ding, F ^a   Liu, Y ^a   Bao, B ^a  

^a JIANGNAN UNIVERSITY   (China)

Author keywords

Least squares; Multivariable system; Parameter estimation; Recursive identification

Indexed keywords

AUTOREGRESSIVE MOVING AVERAGE; GRADIENT BASED; GRADIENT SEARCH; IDENTIFICATION METHOD; INFORMATION VECTOR; INPUT-OUTPUT DATA; ITERATIVE ESTIMATION; LEAST SQUARE; MEASUREMENT DATA; MOTION LAW; MULTI-INPUT MULTI-OUTPUT SYSTEM; MULTI-INPUT MULTI-OUTPUT SYSTEMS; NOISE TERMS; RECURSIVE IDENTIFICATION; SIMULATION TESTS; SYSTEM MODELLING; SYSTEM MODELS; MULTI INPUT MULTI OUTPUT SYSTEMS;

ALGORITHMS; DYNAMICAL SYSTEMS; ESTIMATION; IDENTIFICATION (CONTROL SYSTEMS); ITERATIVE METHODS; MIMO SYSTEMS; MULTIVARIABLE SYSTEMS; SEARCH ENGINES; MULTIPLEXING EQUIPMENT;

PARAMETER ESTIMATION;

EID: 84863012837     PISSN: 09596518     EISSN: 20413041     Source Type: Journal    
DOI: 10.1177/0959651811409491     Document Type: Article

References (44)

1. Liu, Y. J., Xie, L., and Ding, F. An auxiliary model based recursive least-squares parameter estimation algorithm for non-uniformly sampled multirate systems. Proc. IMechE, Part I: J. Systems and Control Engineering, 2009, 223(4), 445-454.
2. Shi, Y. and Fang, H. Kalman filter based identification for systems with randomly missing measurements in a network environment. Int. J. Control, 2010, 83(3), 538-551.
3. Fang, H., Wu, J., and Shi, Y. Genetic adaptive state estimation with missing input/output data. Proc. IMechE, Part I: J. Systems and Control Engineering, 2010, 224(5), 611-617.
4. Ding, F. and Ding, J. Least squares parameter estimation with irregularly missing data. Int. J. Adapt. Control Signal Process., 2010, 24(7), 540-553.
5. Shi, Y., Ding, F., and Chen, T. 2-Norm based recursive design of transmultiplexers with designable filter length. Circuits Syst. Signal Process., 2006, 25(4), 447-462. (Pubitemid 44480296)
6. Yu, B., Shi, Y., and Huang, H. l-2 and l-infinity filtering for multirate systems using lifted models. Circuits Syst. Signal Process., 2008, 27(5), 699-711.
7. Shi, Y., Fang, H., and Yan, M. Kalman filter based adaptive control for networked systems with unknown parameters and randomly missing outputs. Int. J. Robust Nonlin. Control, Special Issue on Control with Limited Information (Part II), 2009, 19(18), 1976-1992.
8. Katayama, S., Yubai, K., and Hirai, J. Iterative design of the reduced-order weight and controller for the H-N loop-shaping method under openloop magnitude constraints for SISO systems. IEEE Trans. Indust. Electron., 2009, 56(10), 3854-3863.
9. Ding, F., Liu, X. P., and Liu, G. Gradient based and least squares based iterative identification methods for OE and OEMA systems. Digital Signal Process., 2010, 20(3), 664-677.
10. Liu, Y. J., Wang, D. Q., and Ding, F. Least squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data. Digital Signal Process., 2010, 20(5), 1458-1467.
11. Liu, X. G. and Lu, J. Least squares based iterative identification for a class of multirate systems. Automatica, 2010, 46(3), 549-554.
12. Scharf, L. L. The SVD and reduced rank signal processing. Signal Process., 1991, 25(2), 113-133.
13. de Lamare, R. C. and Sampaio-Neto, R. Adaptive reduced-rank MMSE filtering with interpolated FIR filters and adaptive interpolators. IEEE Signal Process. Lett., 2005, 12(3), 177-180. (Pubitemid 40319383)
14. de Lamare, R. C. and Sampaio-Neto, R. Reduced-rank adaptive filtering based on joint iterative optimization of adaptive filters. IEEE Signal Process. Lett., 2007, 14(12) 980-983. (Pubitemid 350201258)
15. de Lamare, R. C. and Sampaio-Neto, R. Adaptive reduced-rank processing based on joint and iterative interpolation, decimation, and filtering. IEEE Trans. Signal Process., 2009, 57(7), 2503-2514.
16. Honig, M. L. and Goldstein, J. S. Adaptive reduced-rank interference suppression based on the multistage Wiener filter. IEEE Trans. Commun., 2001, 50(6), 986-994.
17. Qian, H. and Batalama, S. N. Data record-based criteria for the selection of an auxiliary vector estimator of the MMSE/MVDR filter. IEEE Trans. Commun., 2003, 51(10), 1700-1708.
18. Ding, F., Liu, G., and Liu, X. P. Partially coupled stochastic gradient identification methods for non-uniformly sampled systems. IEEE Trans. Autom. Control, 2010, 55(8), 1976-1981.
19. Ding, F. and Chen, T. Performance analysis of multi-innovation gradient type identification methods. Automatica, 2007, 43(1), 1-14. (Pubitemid 44792035)
20. Ding, F., Liu, X. P., and Liu, G. Multi-innovation least squares identification for system modelling. IEEE Trans. Syst. Man Cybernet., Part B: Cybernet., 2010, 40(3), 767-778.
21. Han, L. L. and Ding, F. Multi-innovation stochastic gradient algorithms for multi-input multi-output systems. Digital Signal Process., 2009, 19(4), 545-554.
22. Zhang, J. B., Ding, F., and Shi, Y. Self-tuning control based on multi-innovation stochastic gradient parameter estimation. Syst. Control Lett., 2009, 58(1), 69-75.
23. Ding, F., Liu, X.P., and Liu, G. Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises. Signal Process., 2009, 89(10), 1883-1890.
24. Liu, Y. J., Xiao, Y. S., and Zhao, X. L. Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model. Appl. Math. Comput., 2009, 215(4), 1477-1483.
25. Xie, L., Yang, H. Z., and Ding, F. Modeling and identification for non-uniformly periodically sampled-data systems. IET Control Theor. Applic., 2010, 4(5), 784-794.
26. Wang, D. Q. and Ding, F. Performance analysis of the auxiliary models based multi-innovation stochastic gradient estimation algorithm for output error systems. Digital Signal Process., 2010, 20(3), 750-762.
27. Liu, Y. J., Yu, L., and Ding, F. Multi-innovation extended stochastic gradient algorithm and its performance analysis. Circuits Syst. Signal Process., 2010, 29(4), 649-667.
28. Ding, F. Several multi-innovation identification methods. Digital Signal Process., 2010, 20(4), 1027-1039.
29. Xiao, Y. S., Zhang, Y., Ding, J., and Dai, J. Y. The residual based interactive least squares algorithms and simulation studies. Comput. Math. Applic., 2009, 58(6), 1190-1197.
30. Wang, L. Y., Xie, L., and Wang, X. F. The residual based interactive stochastic gradient algorithms for controlled moving average models. Appl. Math. Comput., 2009, 211(2), 442-449.
31. Ding, F. and Chen, T. Hierarchical gradient-based identification of multivariable discrete-time systems. Automatica, 2005, 41(2), 315-325. (Pubitemid 41536582)
32. Liu, Y. J., Sheng, J., and Ding, R. F. Convergence of stochastic estimation gradient algorithm for multi-variable ARX-like systems. Comput. Math. Applic., 2010, 59(8), 2615-2627.
33. Xiang, L.L., Xie, L. B., Liao, Y. W., and Ding, R. F. Hierarchical least squares algorithms for single-input multiple-output systems based on the auxiliary model. Math. Comput. Modell., 2010, 52(5-6), 918-924.
34. Ding, F., Qiu, L., and Chen, T. Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems. Automatica, 2009, 45(2), 324-332.
35. Chen, J., Zhang, Y., and Ding, R. F. Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems. Math. Comput. Modell., 2010, 52(9-10), 1428-1434.
36. Bao, B., Xu, Y. Q., Sheng, J., and Ding, R. F. Least squares based iterative parameter estimation algorithm for multivariable controlled ARMA system modelling with finite measurement data. Math. Comput. Modell., 2011, 53(9-10), 1664-1669.
37. Ljung, L. System identification: Theory for the user, 2nd edition, 1999 (Prentice-Hall, Englewood Cliffs, New Jersey).
38. Goodwin, G. C. and Sin, K. S. Adaptive filtering, prediction and control, 1984 (Prentice-Hall, Englewood Cliffs, New Jersey).
39. Wang, D. Q., Yang, G. W., and Ding, R. F. Gradient-based iterative parameter estimation for Box- Jenkins systems. Comput. Math. Applic., 2010, 60(5), 1200-1208.
40. Ding, F., Shi, Y., and Chen, T. Auxiliary model based least-squares identification methods for Hammerstein output-error systems. Syst. Control Lett., 2007, 56(5), 373-380. (Pubitemid 46435731)
41. Wang, D. Q. and Ding, F. Extended stochastic gradient identification algorithms for Hammerstein- Wiener ARMAX Systems. Comput. Math. Applic., 2008, 56(12), 3157-3164.
42. Wang, D. Q., Chu, Y. Y., Yang, G. W., and Ding, F. Auxiliary model-based recursive generalized least squares parameter estimation for Hammerstein OEAR systems. Math. Comput. Modell., 2010, 52(1-2), 309-317.
43. Ding, F., Liu, X. P., and Liu, G. Identification methods for Hammerstein nonlinear systems. Digital Signal Process., 2011, 21(2), 215-238.
44. Wang, D. Q. and Ding, F. Least squares based and gradient based iterative identification for Wiener nonlinear systems. Signal Process., 2011, 91(5), 1182-1189.