Have a personal or library account? Click to login
New application of the key term separation principle Cover

New application of the key term separation principle

By: Jozef Vörös  
Open Access
|Dec 2022

References

  1. [1] J. Vörös, “Identification of nonlinear dynamic systems using extended Hammerstein and Wiener models”, Control-Theory and Advanced Technology, vol. 10, no. 4, pp. 1203-1212, 1995.
  2. [2] J. Vörös, “Iterative algorithm for parameter identification of Hammerstein systems with two-segment nonlinearities”, IEEE Trans. Automatic Control, vol. 44, no. 11, pp. 2145-2149, 1999.10.1109/9.802933
  3. [3] J. Vörös, “Modeling and identification of hysteresis using special forms of the Coleman-Hodgdon model”, J. Electrical Engineering, vol. 60, no. 2, 2009, pp. 100-105, 2009.
  4. [4] J. Li and F. Ding, “Maximum likelihood stochastic gradient estimation for Hammerstein systems with colored noise based on the key term separation technique”, Computers and Mathematics with Applications, vol. 62, no. 11, pp. 4170-4177, 2011.10.1016/j.camwa.2011.09.067
  5. [5] H. Chen, Y. Xiao, and F. Ding, “Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle”, Applied Mathematics and Computation, vol. 247, pp. 1202-1210, 2014.10.1016/j.amc.2014.09.070
  6. [6] H. Chen, F. Ding, and Y. Xiao, “Decomposition-based least squares parameter estimation algorithm for input nonlinear systems using the key term separation technique”, Nonlinear Dynamics, vol. 79, no. 3, pp. 2027-2035, 2015.10.1007/s11071-014-1791-5
  7. [7] X. Wang, F. Ding, T. Hayat, and A. Alsaedi, “Combined state and multi-innovation parameter estimation for an input non-linear state-space system using the key term separation”, IET Control Theory & Applications, vol. 10, no. 13, pp. 1503-1512, 2016.10.1049/iet-cta.2015.1056
  8. [8] J. Ma and F. Ding, “Filtering-Based Multistage Recursive Identification Algorithm for an Input Nonlinear Output-Error Autoregressive System by Using the Key Term Separation Technique”, Circuits, Systems, and Signal Processing, vol. 36, no. 2, pp. 577-599, 2017.10.1007/s00034-016-0333-4
  9. [9] F. Ding, H. Chen, L. Xu, J. Dai, Q. Li, and T. Hayat, “A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation”, Journal of the Franklin Institute, vol. 355, no. 8, pp. 3737-3752, 2018.10.1016/j.jfranklin.2018.01.052
  10. [10] J. Ma, Q. Fei, and W. Xiong, “Sliding Window Iterative Identification of Systems with Asymmetric Preload Nonlinearity Based on the Key Term Separation”, IEEE Access, vol. 7, pp. 36633-36641, 2019.10.1109/ACCESS.2019.2904096
  11. [11] Y. Ji, C. Zhang, Z. Kang, and T. Yu, “Parameter estimation for block-oriented nonlinear systems using the key term separation”, International Journal of Robust and Nonlinear Control, vol. 30, no. 9, pp. 3727-3752, 2020.10.1002/rnc.4961
  12. [12] J. Wang, Y. Ji, and C. Zhang, “Iterative parameter and order identification for fractional-order nonlinear finite impulse response systems using the key term separation”, Int. Journal of Adaptive Control and Signal Processing, vol. 35, no. 8, pp. 1562-1577, 2021.10.1002/acs.3257
  13. [13] J. Vörös, “Parameter identification of Wiener systems with discontinuous nonlinearities”, Systems & Control Letters, vol. 44, no. 5, pp. 363-372, 2001.10.1016/S0167-6911(01)00155-4
  14. [14] J. Vörös, “Modeling and identification of Wiener systems with two-segment nonlinearities”, IEEE Trans. Control Systems Technology, vol. 11, no. 2, pp. 253-257, 2003.10.1109/TCST.2003.809238
  15. [15] J. Vörös, “A new approach to Wiener model parameter estimation,”, 5th Int. Conf. Process Control 2002, Kouty nad Desnou, Czech Republik, 2002.
DOI: https://doi.org/10.2478/jee-2022-0060 | Journal eISSN: 1339-309X | Journal ISSN: 1335-3632
Language: English
Page range: 437 - 441
Submitted on: Oct 17, 2022
|
Published on: Dec 24, 2022
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2022 Jozef Vörös, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.