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Maximum Likelihood-Based Recursive Least-Squares Algorithm for Multivariable Systems with Colored Noises Using the Decomposition Technique

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Abstract

This paper considers the parameter estimation problems for a class of multivariable equation-error systems with colored noises. By using the decomposition technique, a multivariable system is transformed into several subsystems to reduce the computational burden, and a maximum likelihood-based recursive least-squares identification algorithm is developed for estimating the parameters of each subsystem. As a comparison, a multivariable recursive extended least-squares algorithm is presented. The analysis indicates that the proposed algorithm has lower computational complexity than the multivariable recursive extended least-squares algorithm, and the numerical simulation results demonstrate that the proposed method is effective.

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References

  1. Y. Cao, P. Li, Y. Zhang, Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing. Future Gener. Comput. Syst. 88, 279–283 (2018)

    Article  Google Scholar 

  2. Y. Cao, L.C. Ma, S. Xiao et al., Standard analysis for transfer delay in CTCS-3. Chin. J. Electron. 26(5), 1057–1063 (2017)

    Article  Google Scholar 

  3. F.Y. Chen, F. Ding, A. Alsaedi, T. Hayat, Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle. Math. Comput. Simulat. 132, 53–67 (2017)

    Article  MathSciNet  Google Scholar 

  4. H.B. Chen, Y.S. Xiao, F. Ding, Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle. Appl. Math. Comput. 247, 1202–1210 (2014)

    MathSciNet  MATH  Google Scholar 

  5. K. Cohen, A. Nedic, R. Srikant, On projected stochastic gradient descent algorithm with weighted averaging for least squares regression. IEEE Trans. Autom. Control 62(11), 5974–5981 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  6. F. Ding, Decomposition based fast least squares algorithm for output error systems. Signal Process. 93(5), 1235–1242 (2013)

    Article  Google Scholar 

  7. F. Ding, Two-stage least squares based iterative estimation algorithm for CARARMA system modeling. Appl. Math. Model. 37(7), 4798–4808 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  8. J.L. Ding, Recursive and iterative least squares parameter estimation algorithms for multiple-input-output-error systems with autoregressive noise. Circuits Syst. Signal Process. 37(5), 1884–1906 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  9. J.L. Ding, The hierarchical iterative identification algorithm for multi-input-output-error systems with autoregressive noise. Complexity (2017). https://doi.org/10.1155/2017/5292894

    Article  MathSciNet  MATH  Google Scholar 

  10. F. Ding, H.B. Chen, L. Xu, J.Y. Dai, Q.S. Li, T. Hayat, A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation. J. Franklin Inst. 355(8), 3737–3752 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  11. F. Ding, X.G. Liu, J. Chu, Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle. IET Control Theory Appl. 7(2), 176–184 (2013)

    Article  MathSciNet  Google Scholar 

  12. F. Ding, D.D. Meng, J.Y. Dai, Q.S. Li, A. Alsaedi, T. Hayat, Least squares based iterative parameter identification for stochastic dynamical systems with ARMA noise using the model equivalence. Int. J. Control Autom. Syst. 16(2), 630–639 (2018)

    Article  Google Scholar 

  13. F. Ding, F.F. Wang, L. Xu, M.H. Wu, Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering. J. Franklin Inst. 354(3), 1321–1339 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  14. F. Ding, L. Xu, F.E. Alsaadi, T. Hayat, Iterative parameter identification for pseudo-linear systems with ARMA noise using the filtering technique. IET Control Theory Appl. 12(7), 892–899 (2018)

    Article  MathSciNet  Google Scholar 

  15. F. Ding, L. Xu, Q.M. Zhu, Performance analysis of the generalised projection identification for time-varying systems. IET Control Theory Appl. 10(18), 2506–2514 (2016)

    Article  MathSciNet  Google Scholar 

  16. E. Eweda, Stabilization of high-order stochastic gradient adaptive filtering algorithms. IEEE Trans. Signal Process. 65(15), 3948–3959 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  17. M. Gan, C.L.P. Chen, G.Y. Chen, L. Chen, On some separated algorithms for separable nonlinear squares problems. IEEE Trans. Cybern. (2018). https://doi.org/10.1109/TCYB.2017.2751558

    Article  Google Scholar 

  18. M. Gan, H.X. Li, H. Peng, A variable projection approach for efficient estimation of RBF-ARX model. IEEE Trans. Cybern. 45(3), 462–471 (2015)

    Article  Google Scholar 

  19. P.C. Gong, W.Q. Wang, F.C. Li, H. Cheung, Sparsity-aware transmit beamspace design for FDA-MIMO radar. Signal Process. 144, 99–103 (2018)

    Article  Google Scholar 

  20. H.L. Gao, C.C. Yin, The perturbed sparre Andersen model with a threshold dividend strategy. J. Comput. Appl. Math. 220(1–2), 394–408 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  21. P. Li, R. Dargaville, Y. Cao et al., Storage aided system property enhancing and hybrid robust smoothing for large-scale PV systems. IEEE Trans. Smart Grid 8(6), 2871–2879 (2017)

    Article  Google Scholar 

  22. P. Li, R.X. Li, Y. Cao, G. Xie, Multi-objective sizing optimization for island microgrids using triangular aggregation model and Levy-Harmony algorithm. IEEE Trans. Ind. Inf. (2018). https://doi.org/10.1109/TII.2017.2778079

    Article  Google Scholar 

  23. M.H. Li, X.M. Liu, The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique. Signal Process. 147, 23–34 (2018)

    Article  Google Scholar 

  24. M.H. Li, X.M. Liu, Auxiliary model based least squares iterative algorithms for parameter estimation of bilinear systems using interval-varying measurements. IEEE Access 6, 21518–21529 (2018)

    Article  Google Scholar 

  25. F. Liu, A note on Marcinkiewicz integrals associated to surfaces of revolution. J. Aust. Math. Soc. 104(3), 380–402 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  26. F. Liu, Continuity and approximate differentiability of multisublinear fractional maximal functions. Math. Inequal. Appl. 21(1), 25–40 (2018)

    MathSciNet  MATH  Google Scholar 

  27. F. Liu, On the Triebel–Lizorkin space boundedness of Marcinkiewicz integrals along compound surfaces. Math. Inequal. Appl. 20(2), 515–535 (2017)

    MathSciNet  MATH  Google Scholar 

  28. F. Liu, H.X. Wu, Singular integrals related to homogeneous mappings in Triebel–Lizorkin spaces. J. Math. Inequal. 11(4), 1075–1097 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  29. F. Liu, H.X. Wu, Regularity of discrete multisublinear fractional maximal functions. Sci. China Math. 60(8), 1461–1476 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  30. F. Liu, H.X. Wu, On the regularity of maximal operators supported by submanifolds. J. Math. Anal. Appl. 453(1), 144–158 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  31. F. Liu, Q.Y. Xue, K. Yabuta, Rough maximal singular integral and maximal operators supported by subvarieties on Triebel–Lizorkin spaces. Nonlinear Anal. 171, 41–72 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  32. P. Ma, F. Ding, Q.M. Zhu, Decomposition-based recursive least squares identification methods for multivariate pseudolinear systems using the multi-innovation. Int. J. Syst. Sci. 49(5), 920–928 (2018)

    Article  Google Scholar 

  33. J.X. Ma, W.L. Xiong, J. Chen et al., Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter. IET Control Theory Appl. 11(6), 857–869 (2017)

    Article  MathSciNet  Google Scholar 

  34. Y.W. Mao, F. Ding, A novel parameter separation based identification algorithm for Hammerstein systems. Appl. Math. Lett. 60, 21–27 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  35. J. Pan, H. Ma, X. Jiang, et al., Adaptive gradient-based iterative algorithm for multivariate controlled autoregressive moving average systems using the data filtering technique. Complexity 2018, 9598307. https://doi.org/10.1155/2018/9598307

  36. Z.H. Rao, C.Y. Zeng, M.H. Wu et al., Research on a handwritten character recognition algorithm based on an extended nonlinear kernel residual network. KSII Trans. Int. Inf. Syst. 12(1), 413–435 (2018)

    Google Scholar 

  37. D.Q. Wang, Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models. Appl. Math. Lett. 57, 13–19 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  38. Y.J. Wang, F. Ding, Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model. Automatica 71, 308–313 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  39. Y.J. Wang, F. Ding, A filtering based multi-innovation gradient estimation algorithm and performance analysis for nonlinear dynamical systems. IMA J. Appl. Math. 82(6), 1171–1191 (2017)

    Article  MathSciNet  Google Scholar 

  40. Y.J. Wang, F. Ding, L. Xu, Some new results of designing an IIR filter with colored noise for signal processing. Digital Signal Process. 72, 44–58 (2018)

    Article  MathSciNet  Google Scholar 

  41. D.Q. Wang, Z. Zhang, J.Y. Yuan, Maximum likelihood estimation method for dual-rate Hammerstein systems. Int. J. Control Autom. Syst. 15(2), 698–705 (2017)

    Article  Google Scholar 

  42. D.Q. Wang, Y.P. Gao, Recursive maximum likelihood identification method for a multivarable controlled autoregressive moving average system. IMA J. Math. Control Inf. 33(4), 1015–1031 (2016)

    Article  MATH  Google Scholar 

  43. L. Xu, The parameter estimation algorithms based on the dynamical response measurement data. Adv. Mech. Eng. 9(11), 1–12 (2017). https://doi.org/10.1177/1687814017730003

    Article  Google Scholar 

  44. L. Xu, F. Ding, Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling. Circuits Syst. Signal Process. 36(4), 1735–1753 (2017)

    Article  MATH  Google Scholar 

  45. L. Xu, F. Ding, Iterative parameter estimation for signal models based on measured data. Circuits Syst. Signal Process. 37(7), 3046–3069 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  46. L. Xu, F. Ding, Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle. IET Signal Process. 11(2), 228–237 (2017)

    Article  Google Scholar 

  47. L. Xu, F. Ding, Parameter estimation for control systems based on impulse responses. Int. J. Control Autom. Syst. 15(6), 2471–2479 (2017)

    Article  Google Scholar 

  48. L. Xu, F. Ding, Y. Gu, A. Alsaedi, T. Hayat, A multi-innovation state and parameter estimation algorithm for a state space system with d-step state-delay. Signal Process. 140, 97–103 (2017)

    Article  Google Scholar 

  49. G.H. Xu, Y. Shekofteh, A. Akgul, C.B. Li, S. Panahi, A new chaotic system with a self-excited attractor: entropy measurement, signal encryption, and parameter estimation. Entropy (2018). https://doi.org/10.3390/e20020086

    Article  Google Scholar 

  50. L. Xu, W.L. Xiong, A. Alsaedi, T. Hayat, Hierarchical parameter estimation for the frequency response based on the dynamical window data. Int. J. Control Autom. Syst. (2018). https://doi.org/10.1007/s12555-017-0482-7

    Article  Google Scholar 

  51. C.C. Yin, C.W. Wang, The perturbed compound Poisson risk process with investment and debit interest. Methodol. Comput. Appl. Probab. 12(3), 391–413 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  52. C.C. Yin, Y.Z. Wen, Exit problems for jump processes with applications to dividend problems. J. Comput. Appl. Math. 245, 30–52 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  53. C.C. Yin, Y.Z. Wen, Optimal dividend problem with a terminal value for spectrally positive Levy processes. Insur. Math. Econ. 53(3), 769–773 (2013)

    Article  MATH  Google Scholar 

  54. C.C. Yin, K.C. Yuen, Optimality of the threshold dividend strategy for the compound Poisson model. Stat. Probab. Lett. 81(12), 1841–1846 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  55. C.C. Yin, J.S. Zhao, Nonexponential asymptotics for the solutions of renewal equations, with applications. J. Appl. Probab. 43(3), 815–824 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  56. P.C. Young, Refined instrumental variable estimation: maximum likelihood optimization of a unified Box–Jenkins model. Automatica 52, 35–46 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  57. Y.Z. Zhang, Y. Cao, Y.H. Wen, L. Liang, F. Zou, Optimization of information interaction protocols in cooperative vehicle-infrastructure systems. Chin. J. Electron. 27(2), 439–444 (2018)

    Article  Google Scholar 

  58. X. Zhang, F. Ding, F.E. Alsaadi, T. Hayat, Recursive parameter identification of the dynamical models for bilinear state space systems. Nonlinear Dyn. 89(4), 2415–2429 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  59. X. Zhang, F. Ding, L. Xu, E.F. Yang, State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle. IET Control Theory Appl. 12(12), 1704–1713 (2018)

    Article  MathSciNet  Google Scholar 

  60. X. Zhang, L. Xu, F. Ding, T. Hayat, Combined state and parameter estimation for a bilinear state space system with moving average noise. J. Franklin Inst. 355(6), 3079–3103 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  61. N. Zhao, R. Liu, Y. Chen, M. Wu et al., Contract design for relay incentive mechanism under dual asymmetric information in cooperative networks. Wireless Netw. (2018). https://doi.org/10.1007/s11276-017-1518-x

    Article  Google Scholar 

  62. D.Q. Zhu, X. Cao, B. Sun, C.M. Luo, Biologically inspired self-organizing map applied to task assignment and path planning of an AUV system. IEEE Trans. Cognit. Dev. Syst. 10(2), 304–313 (2018)

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to Professor Feng Ding at the Jiangnan University for his helpful suggestions and the main idea of this work comes from him and his books. This work was supported by the National Natural Science Foundation of China (No. 61273194) and the 111 Project (B12018).

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Correspondence to Huafeng Xia.

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Xia, H., Ji, Y., Xu, L. et al. Maximum Likelihood-Based Recursive Least-Squares Algorithm for Multivariable Systems with Colored Noises Using the Decomposition Technique. Circuits Syst Signal Process 38, 986–1004 (2019). https://doi.org/10.1007/s00034-018-0904-7

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