Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems

Mathematical and Computer Modelling

Volumn 52, Issue 9-10, 2010, Pages 1428-1434

  Chen, Jing ^a,c   Zhang, Yan ^b   Ding, Ruifeng ^c  

^a Wuxi Professional College of Science and Technology   (China)

^b WUXI INSTITUTE OF TECHNOLOGY   (China)

^c JIANGNAN UNIVERSITY   (China)

Author keywords

Auxiliary model identification; Multi innovation identification; Multi input multi output systems; Parameter estimation; Stochastic gradient

Indexed keywords

AUXILIARY MODELS; IDENTIFICATION MODEL; IDENTIFICATION PROBLEM; INFORMATION VECTOR; INNOVATION MATRIX; MULTI-INNOVATION IDENTIFICATION; MULTI-INPUT MULTI-OUTPUT NONLINEAR SYSTEMS; MULTI-INPUT MULTI-OUTPUT SYSTEMS; MULTIVARIABLE NONLINEAR SYSTEMS; NUMERICAL EXAMPLE; PARAMETER IDENTIFICATION; RECURSIONS; STOCHASTIC GRADIENT; STOCHASTIC GRADIENT ALGORITHMS;

ALGORITHMS; INNOVATION; METAL ANALYSIS; NONLINEAR SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

PARAMETER ESTIMATION;

EID: 77956010793     PISSN: 08957177     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mcm.2010.05.026     Document Type: Article

References (36)

1. Kohandel M., Sivaloganathan S., Tenti G. Estimation of the quasi-linear viscoelastic parameters using a genetic algorithm. Mathematical and Computer Modelling 2008, 47(3-4):266-270.
2. Figueroa J.L., Biagiola S.I., Agamennoni O.E. An approach for identification of uncertain Wiener systems. Mathematical and Computer Modelling 2008, 48(1-2):305-315.
3. Ding J., Han L.L., Chen X.M. Time series AR modeling with missing observations based on the polynomial transformation. Mathematical and Computer Modelling 2010, 51(5-6):527-536.
4. Han L.L., Sheng J., Ding F., Shi Y. Auxiliary model identification method for multirate multi-input systems based on least squares. Mathematical and Computer Modelling 2009, 50(7-8):1100-1106.
5. Fang J., Leyman A.R., Chew Y.H., Duan H.P. Some further results on blind identification of MIMO FIR channels via second-order statistics. Signal Processing 2007, 87(6):1434-1447.
6. Mahmoudi A., Karimi M. Estimation of the parameters of multi-channel autoregressive signals from noisy observations. Signal Processing 2008, 88(11):2777-2783.
7. Zhao S.K., Man Z.H., Khoo S.Y., Wu H.R. Variable step-size LMS algorithm with a quotient form. Signal Processing 2009, 89(1):67-76.
8. Goodwin G.C., Sin K.S. Adaptive Filtering Prediction and Control 1984, Prentice-Hall, Englewood Cliffs, NJ.
9. Ding F., Yang H.Z., Liu F. Performance analysis of stochastic gradient algorithms under weak conditions. Science in China Series F-Information Sciences 2008, 51(9):1269-1280.
10. Ding F., Liu P.X., Yang H.Z. Parameter identification and intersample output estimation for dual-rate systems. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 2008, 38(4):966-975.
11. Ding F., Chen T. Performance analysis of multi-innovation gradient type identification methods. Automatica 2007, 43(1):1-14.
12. Ding F., Liu P.X., Liu G. Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises. Signal Processing 2009, 89(10):1883-1890.
13. Zhang J.B., Ding F., Shi Y. Self-tuning control based on multi-innovation stochastic gradient parameter estimation. Systems & Control Letters 2009, 58(1):69-75.
14. Han L.L., Ding F. Multi-innovation stochastic gradient algorithms for multi-input multi-output systems. Digital Signal Processing 2009, 19(4):545-554.
15. Han L.L., Ding F. Identification for multirate multi-input systems using the multi-innovation identification theory. Computers & Mathematics with Applications 2009, 57(9):1438-1449.
16. Wang D.Q., Ding F. Performance analysis of the auxiliary models based multi-innovation stochastic gradient estimation algorithm for output error systems. Digital Signal Processing 2010, 20(3):750-762.
17. Liu Y.J., Xiao Y.S., Zhao X.L. Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model. Applied Mathematics and Computation 2009, 215(4):1477-1483.
18. Ding F., Chen H.B., Li M. Multi-innovation least squares identification methods based on the auxiliary model for MISO systems. Applied Mathematics and Computation 2007, 187(2):658-668.
19. Xie L., Yang H.Z., Ding F. Modeling and identification for non-uniformly periodically sampled-data systems. IET Control Theory & Applications 2010, 4(5):784-794.
20. Ding F., Liu P.X., Liu G. Multi-innovation least squares identification for linear and pseudo-linear regression models. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2010, 40(3):767-778.
21. Ding F. Several multi-innovation identification methods. Digital Signal Processing 2010, 20(4):1027-1039.
22. Wang D.Q., Chu Y.Y., Ding F. Auxiliary model-based RELS and MI-ELS algorithms for Hammerstein OEMA systems. Computers & Mathematics with Applications 2010, 59(9):3092-3098.
23. Wang D.Q., Ding F. Input-output data filtering based recursive least squares parameter estimation for CARARMA systems. Digital Signal Processing 2010, 20(4):991-999.
24. Ding F., Shi Y., Chen T. Gradient-based identification methods for Hammerstein nonlinear ARMAX models. Nonlinear Dynamics 2006, 45(1-2):31-43.
25. Ding J., Shi Y., Wang H.G., Ding F. A modified stochastic gradient based parameter estimation algorithm for dual-rate sampled-data systems. Digital Signal Processing 2010, 20(4):1238-1249.
26. Ding F., Liu P.X., Liu G. Gradient based and least-squares based iterative identification methods for OE and OEMA systems. Digital Signal Processing 2010, 20(3):664-677.
27. Liu Y.J., Wang D.Q., Ding F. Least-squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data. Digital Signal Processing 2010, 20(5):1458-1467.
28. Han H.Q., Xie L., Ding F., Liu X.G. Hierarchical least squares based iterative identification for multivariable systems with moving average noises. Mathematical and Computer Modelling 2010, 51(9-10):1213-1220.
29. Ding F., Chen T. Identification of Hammerstein nonlinear ARMAX systems. Automatica 2005, 41(9):1479-1489.
30. Wang D.Q., Chu Y.Y., Yang G.W., Ding F. Auxiliary model-based recursive generalized least squares parameter estimation for Hammerstein OEAR systems. Mathematical and Computer Modelling 2010, 52(1-2):309-317.
31. Wang D.Q., Ding F. Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems. Computers Mathematics with Applications 2008, 56(12):3157-3164.
32. Ding F., Chen T. Combined parameter and output estimation of dual-rate systems using an auxiliary model. Automatica 2004, 40(10):1739-1748.
33. Ding F., Chen T. Parameter estimation of dual-rate stochastic systems by using an output error method. IEEE Transactions on Automatic Control 2005, 50(9):1436-1441.
34. Ding F., Shi Y., Chen T. Auxiliary model based least-squares identification methods for Hammerstein output-error systems. Systems & Control Letters 2007, 56(5):373-380.
35. Liu Y.J., Xie L., Ding F. An auxiliary model based recursive least squares parameter estimation algorithm for non-uniformly sampled multirate systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 2009, 223(4):445-454.
36. Ding F., Chen T. Identification of dual-rate systems based on finite impulse response models. International Journal of Adaptive Control and Signal Processing 2004, 18(7):589-598.