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Wiener model-based system identification using moth flame optimised Kalman filter algorithm

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Abstract

The primary aim of this paper is to pursue the optimal set of Wiener system coefficients using the meta-heuristic optimisation technique-based Kalman filter (KF). The best estimation of unknown parameters of a dynamic system by the basic KF approach majorly depends upon the efficiently tuned KF parameters such as process and observation noise covariance matrices; otherwise, it would lead to descent of the filter performance. To defeat the aforesaid difficulty, an advanced optimisation technique called the moth flame optimisation (MFO) algorithm is used. In this work, the proposed method proceeds through the following two steps for unknown Wiener system parameters estimation. Initially, the MFO algorithm generates the near-global optimised KF parameters by designing the suitable cost function. Finally, the unknown Wiener model parameters are determined by using the basic KF approach based on optimised KF parameters derived in the earlier step. The reliability, accuracy and robustness of the proposed MFO–KF approach are analysed on a benchmark numerical problem and three practical nonlinear plants. The results indicate that the MFO–KF method shows better estimations compared to the other employed benchmark optimisation algorithms-based KF methods as well as results from other recently reported works.

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References

  1. Kargar, H., Zarei, J., Razavi-Far, R.: Robust fault detection filter design for nonlinear networked control systems with time-varying delays and packet dropout. Circuits Syst. Signal Process. 38(1), 63–84 (2019)

    Article  MathSciNet  Google Scholar 

  2. Pal, P.S., Kar, R., Mandal, D., Ghoshal, S.P.: A hybrid backtracking search algorithm with wavelet mutation-based nonlinear system identification of Hammerstein models. SIViP 11, 929–936 (2017)

    Article  Google Scholar 

  3. Mehmood, A., Aslam, M.S., Chaudhary, N.I., Zameer, A., Raja, M.A.Z.: Parameter estimation for Hammerstein control autoregressive systems using differential evolution. SIViP 12(8), 1603–1610 (2018)

    Article  Google Scholar 

  4. Wigren, T.: Recursive prediction error identification using the nonlinear Wiener model. Automatica 29(4), 1011–1025 (1993)

    Article  MathSciNet  Google Scholar 

  5. Kazemi, M., Arefi, N.M.: A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems. ISA Trans. 67, 382–388 (2017)

    Article  Google Scholar 

  6. Xiong, W., Wang, X., Ke, L., Xu, B.: EM algorithm based identification of a class of nonlinear Wiener systems with missing output data. Nonlinear Dyn. 80, 329–339 (2015)

    Article  MathSciNet  Google Scholar 

  7. Li, J., Zong, T., Gu, J., Hua, L.: Parameter estimation of Wiener systems based on the particle swarm iteration and gradient search principle. Circuits Syst. Signal Process. 39, 3470–3495 (2020)

    Article  Google Scholar 

  8. Zhang, J.H., Xia, P.Q.: An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models. J. Sound Vib. 389, 153–167 (2017)

    Article  Google Scholar 

  9. Zheng, Y.X., Liao, Y.: Parameter identification of non-linear dynamic systems using an improved particle swarm optimization. Optik 127(19), 7865–7874 (2016)

    Article  Google Scholar 

  10. Pal, P.S., Kar, R., Mandal, D., Ghoshal, S.P.: Parametric identification with performance assessment of Wiener systems using brain storm optimization algorithm. Circuits Syst. Signal Process. 36, 3143–3181 (2017)

    Article  Google Scholar 

  11. Janjanam, L., Saha, S.K., Kar, R., Mandal, D.: An efficient identification approach for highly complex non-linear systems using the evolutionary computing method based Kalman filter. Int. J. Electron. Commun. 138, 153890 (2021)

    Article  Google Scholar 

  12. Wang, Z., An, H., Luo, X.: Adaptive filtering-based recursive identification for time-varying Wiener output-error systems with unknown noise statistics. J. Franklin Inst. 357, 1280–1298 (2020)

    Article  MathSciNet  Google Scholar 

  13. Wang, X., Zhu, F., Ding, F.: The modified extended Kal- man filter-based recursive estimation for Wiener non-linear systems with process noise and measurement noise. Int. J. Adapt. Control Signal Process. 34, 1321–1340 (2020)

    Article  Google Scholar 

  14. Huang, Y.L., Zhang, Y.G., Wu, Z.M., Li, N., Chambers, J.: A novel adaptive Kalman filter with the inaccurate process and measurement noise covariance matrices. IEEE Trans. Autom. Control 63(2), 594–601 (2018)

    Article  MathSciNet  Google Scholar 

  15. Kim, T., Adhikari, A., Pandey, R.: An onboard model based condition monitoring for lithium-ion batteries. IEEE Trans. Ind. Appl. 55(2), 1835–1843 (2019)

    Article  Google Scholar 

  16. Zhang, Z., Huang, C., Ding, D., Tang, S., Han, B., Huang, H.: Hummingbirds optimisation algorithm-based particle filter for manoeuvring target tracking. Nonlinear Dyn. 97(2), 1227–1243 (2019)

    Article  Google Scholar 

  17. Yazid, E., Liew, M.S., Parman, S., Kurian, V.J.: Improving the modelling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter. Appl. Soft Comput. 35, 695–707 (2015)

    Article  Google Scholar 

  18. Janjanam, L., Saha, S.K., Kar, R., Mandal, D.: Global gravitational search algorithm-aided Kalman filter design for Volterra-based nonlinear system identification. Circuits Syst. Signal Process. 40(5), 2302–2334 (2020)

    Article  Google Scholar 

  19. Lee, K.H., Baek, S.W., Kim, K.W.: Inverse radiation analysis using repulsive particle swarm optimisation algorithm. Int. J. Heat Mass Transf. 51, 2772–2783 (2008)

    Article  Google Scholar 

  20. Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188, 1567–1579 (2007)

    Article  MathSciNet  Google Scholar 

  21. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching learning-based optimization: An optimization method for continuous non-linear large scale problems. Inf. Sci. 183(1), 1–15 (2012)

    Article  MathSciNet  Google Scholar 

  22. Mirjalili, S.: Moth-flame optimisation algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)

    Article  Google Scholar 

  23. Nayak, C., Saha, S.K., Kar, R., Mandal, D.: Automated QRS complex detection using MFO-based DFOD. IET Signal Proc. 12(9), 1172–1184 (2018)

    Article  Google Scholar 

  24. Elrady, E.A., Gan, L.: Identification of Hammerstein and Wiener models using spectral magnitude matching. In: Proceeding of the 17th World Congress on the International Federation of Automatic Control, Seoul, Korea, pp. 6440–6445 (2008)

  25. Wang, J.S., Hsu, Y.L.: Dynamic nonlinear system identification using a Wiener-type recurrent network with OKID algorithm. J. Inf. Sci. Eng. 24(3), 891–905 (2008)

    Google Scholar 

  26. Mahata, S., Saha, S.K., Kar, R., Mandal, D.: Optimal design of wideband digital integrator and differentiators using harmony search algorithm. Int. J. Numer. Model. 30, e2203 (2017)

    Article  Google Scholar 

  27. Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers, 3rd edn. Wiley, New York (2003)

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492010, India

    Lakshminarayana Janjanam & Suman Kumar Saha

  2. Department of Electronics and Communication Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

    Rajib Kar & Durbadal Mandal

Authors
  1. Lakshminarayana Janjanam
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  2. Suman Kumar Saha
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  3. Rajib Kar
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  4. Durbadal Mandal
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Correspondence to Lakshminarayana Janjanam.

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Janjanam, L., Saha, S.K., Kar, R. et al. Wiener model-based system identification using moth flame optimised Kalman filter algorithm. SIViP 16, 1425–1433 (2022). https://doi.org/10.1007/s11760-021-02096-w

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  • DOI: https://doi.org/10.1007/s11760-021-02096-w

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