Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering

Journal of the Franklin Institute

Volumn 354, Issue 3, 2017, Pages 1321-1339

  Ding, Feng ^a,b   Wang, Feifei ^a   Xu, Ling ^a   Wu, Minghu ^b  

^a JIANGNAN UNIVERSITY   (China)

^b HUBEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER NETWORKS; CONTROL;

AUTOREGRESSIVE MOVING AVERAGE; COMPUTATIONAL EFFORT; DATA FILTERING; HIERARCHICAL IDENTIFICATION; ITERATIVE ALGORITHM; ITERATIVE IDENTIFICATION; NOCV1; ORIGINAL SYSTEMS; PSEUDO LINEAR;

ITERATIVE METHODS;

EID: 85008186357     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2016.11.030     Document Type: Article

References (43)

1. [1] Goodwin, G.C., Sin, K.S., Adaptive Filtering Prediction and Control. 1984, Prentice Hall, Englewood Cliffs, New Jersey.
2. [2] Ljung, L., System Identification: Theory for the User. 1999, Prentice Hall, Englewood Cliffs, New Jersey.
3. [3] Xu, L., Application of the Newton iteration algorithm to the parameter estimation for dynamical systems. J. Comput. Appl. Math. 288 (2015), 33–43.
4. [4] Xu, L., Chen, L., Xiong, W.L., Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration. Nonlinear Dyn. 79:3 (2015), 2155–2163.
5. [5] Xu, L., The damping iterative parameter identification method for dynamical systems based on the sine signal measurement. Signal Process. 120 (2016), 660–667.
6. [6] Xu, L., Ding, F., Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling. Circuits Syst. Signal Process., 36, 2016, 10.1007/s00034-016-0378-4.
7. [7] Xu, L., A proportional differential control method for a time-delay system using the Taylor expansion approximation. Appl. Math. Comput. 236 (2014), 391–399.
8. [8] Li, H., Xie, P., Yan, W., Receding horizon formation tracking control of constrained underactuated autonomous underwater vehicles. IEEE Trans. Ind. Electron., 2016, 10.1109/TIE.2016.2589921.
9. [9] Li, H., Yan, W., Model predictive stabilization of constrained underactuated autonomous underwater vehicles with guaranteed feasibility and stability. IEEE/ASME Trans. Mechatron., 2016, 10.1109/TMECH.2016.2587288.
10. [10] Li, H., Shi, Y., Yan, W., On neighbor information utilization in distributed receding horizon control for consensus-seeking. IEEE Trans. Cybern. 46:9 (2016), 2019–2027.
11. [11] Li, H., Shi, Y., Yan, W., Distributed receding horizon control of constrained nonlinear vehicle formations with guaranteed γ-gain stability. Automatica 68 (2016), 148–154.
12. [12] Xie, L., Yang, H.Z., Ding, F., Huang, B., Novel model of non-uniformly sampled-data systems based on a time-varying backward shift operator. J. Process Control 43 (2016), 38–52.
13. [13] Xia, Y., Deng, Z., Zheng, W.X., Analysis and application of a novel fast algorithm for 2-D ARMA model parameter estimation. Automatica 49:10 (2013), 3056–3064.
14. [14] Xia, Y., Zheng, W.X., Novel parameter estimation of autoregressive signals in the presence of noise. Automatica 62 (2015), 98–105.
15. [15] Xia, Y., Kamel, M.S., Leung, H., A fast algorithm for AR parameter estimation using a novel noise-constrained least-squares method. Neural Netw. 23:3 (2010), 396–405.
16. [16] Wang, T.Z., Qi, J., Xu, H., et al. Fault diagnosis method based on FFT-RPCA-SVM for cascaded-multilevel inverter. ISA Trans. 60 (2016), 156–163.
17. [17] Wang, T.Z., Wu, H., Ni, M.Q., et al. An adaptive confidence limit for periodic non-steady conditions fault detection. Mech. Syst. Signal Process. 72–73 (2016), 328–345.
18. [18] Cao, X., Zhu, D.Q., Yang, S.X., Multi-AUV target search based on bioinspired neurodynamics model in 3-D underwater environments. IEEE Trans. Neural Netw. Learn. Syst. 27:11 (2016), 2364–2374.
19. [19] Chu, Z.Z., Zhu, D.Q., Yang, S.X., Observer-based adaptive neural network trajectory tracking control for remotely operated vehicle. IEEE Trans. Neural Netw. Learn. Syst., 2016, 10.1109/TNNLS.2016.2544786.
20. [20] Pan, J., Yang, X.H., Cai, H.F., Mu, B.X., Image noise smoothing using a modified Kalman filter. Neurocomputing 173 (2016), 1625–1629.
21. [21] Wang, C., Zhu, L., Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory. J. Frankl. Inst. 352:10 (2015), 4624–4637.
22. [22] Wang, Y.J., Ding, F., Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model. Automatica 71 (2016), 308–313.
23. [23] Wang, Y.J., Ding, F., The auxiliary model based hierarchical gradient algorithms and convergence analysis using the filtering technique. Signal Process. 128 (2016), 212–221.
24. [24] Liu, Y., Bai, E.W., Iterative identification of Hammerstein systems. Automatica 43:2 (2007), 346–354.
25. [25] Liu, X.G., Lu, J., Least squares based iterative identification for a class of multirate systems. Automatica 46:3 (2010), 549–554.
26. [26] Vörös, J., Parameter identification of discontinuous Hammerstein systems. Automatica 33:6 (1997), 1141–1146.
27. [27] Vörös, J., Modeling and identification of systems with backlash. Automatica 46:2 (2010), 369–374.
28. [28] Ding, F., Liu, X.M., Gu, Y., 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.
29. [29] Ding, F., Liu, X.M., Ma, X.Y., Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition. J. Comput. Appl. Math. 301 (2016), 135–143.
30. [30] Wang, D.Q., Zhang, W., Improved least squares identification algorithm for multivariable Hammerstein systems. J. Frankl. Inst. 352:11 (2015), 5292–5307.
31. [31] Wang, D.Q., Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models. Appl. Math. Lett. 57 (2016), 13–19.
32. [32] Wang, Y.J., Ding, F., Recursive parameter estimation algorithms and convergence for a class of nonlinear systems with colored noise. Circuits Syst. Signal Process. 35:10 (2016), 3461–3481.
33. [33] Ding, F., Liu, X.M., Liu, M.M., The recursive least squares identification algorithm for a class of Wiener nonlinear systems. J. Frankl. Inst. 353:7 (2016), 1518–1526.
34. [34] Wang, Y.J., Ding, F., Recursive least squares algorithm and gradient algorithm for Hammerstein-Wiener systems using the data filtering. Nonlinear Dyn. 84:2 (2016), 1045–1053.
35. [35] Xia, Y., Chen, T., Shan, J., A novel iterative method for computing generalized inverse. Neural Comput. 26:2 (2014), 449–465.
36. [36] Wang, Y.J., Ding, F., The filtering based iterative identification for multivariable systems. IET Control Theory Appl. 10:8 (2016), 894–902.
37. [37] Ding, F., Deng, K.P., Liu, X.M., Decomposition based Newton iterative identification method for a Hammerstein nonlinear FIR system with ARMA noise. Circuits Syst. Signal Process. 33:9 (2014), 2881–2893.
38. [38] Wang, F.F., Ding, F., Gradient-based iterative identification methods for multivariate pseudo-linear moving average systems using the data filtering. Nonlinear Dyn. 84:4 (2016), 2003–2015.
39. [39] Guo, L.J., Ding, F., Least squares based iterative algorithm for pseudo-linear autoregressive moving average systems using the data filtering technique. J. Frankl. Inst. 352:10 (2015), 4339–4353.
40. [40] Ding, F., Wang, X.H., Chen, Q.J., Xiao, Y.S., Recursive least squares parameter estimation for a class of output nonlinear systems based on the model decomposition. Circuits Syst. Signal Process. 35:9 (2016), 3323–3338.
41. [41] Wan, X.K., Li, Y., Xia, C., Wu, M.H., Liang, J., Wang, N., A T-wave alternans assessment method based on least squares curve fitting technique. Measurement 86 (2016), 93–100.
42. [42] Feng, L., Wu, M.H., Li, Q.X., et al. Array factor forming for image reconstruction of one-dimensional nonuniform aperture synthesis radiometers. IEEE Geosci. Remote Sens. Lett. 13:2 (2016), 237–241.
43. [43] Ji, Y., Liu, X.M., Unified synchronization criteria for hybrid switching-impulsive dynamical networks. Circuits Syst. Signal Process. 34:5 (2015), 1499–1517.