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All issues Volume 297 (2021) E3S Web Conf., 297 (2021) 01020 References
Open Access
Issue
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
Article Number 01020
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/202129701020
Published online 22 September 2021
  1. A. Chiuso and G. Pillinetto, “System identification: A machine learning perspective,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 2, p. 281–304 (2019). [Google Scholar]
  2. U. Soverini and T. Söderström, “Frequency domain identification of FIR models in the presence of additive input-output noise,” Automatica, vol. 115, p. 108879 (2020). [Google Scholar]
  3. H. Zhang, T. Wang, and Y. Zhao, “Asymptotically Efficient Recursive Identification of FIR Systems With Binary-Valued Observations,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019. [Google Scholar]
  4. J. Guo, Y. Zhao, C. Y. Sun and Y. Yu, “Recursive identification of FIR systems with binary-valued outputs and communication channels,” Automatica, vol. 60, p. 165–172 (2015). [CrossRef] [Google Scholar]
  5. T. Yuan, Q. Liu, and J. Guo, “Identification of FIR Systems with Quantized Input and Binary-Valued Observations Under A Priori Parameter Constraint,” In : 2020 39th Chinese Control Conference (CCC). IEEE, p. 1099–1104 (2020). [Google Scholar]
  6. C. Cortes and V. Vapnik, “Support-vector networks. Machine learning,” vol. 20, no. 3, p. 273–297 (1995). [Google Scholar]
  7. B. Scholkopf and A. J. Smola, “Learning with kernels: support vector machines, regularization, optimization, and beyond,” Adaptive Computation and Machine Learning series, 2018. [Google Scholar]
  8. W. Liu, P. P. Pokharel and S. Haykin, “Kernel adaptive filtering: a comprehensive introduction,” Vol. 57. John Wiley & Sons (2011). [Google Scholar]
  9. W. Liu, P. P. Pokharel and J. C. Principe, “The kernel least-mean-square algorithm,” IEEE Transactions on Signal Processing, vol. 56, no. 2, p. 543–554 (2008). [Google Scholar]
  10. W. Liu and J. C. Principe, “Kernel affine projection algorithms,” EURASIP Journal on Advances in Signal Processing, vol. 2008, p. 1–12 (2008). [Google Scholar]
  11. R. Castro Garcia, “Structured nonlinear system identification using kernel-based methods,” 2017. [Google Scholar]
  12. B. Chen, S. Zhao, P. Zhu and J. C. Principe, “Quantized kernel least mean square algorithm,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 1, p. 22–32 (2011). [Google Scholar]
  13. S. Wang, Y. Zheng, and C. Ling, “Regularized kernel least mean square algorithm with multiple-delay feedback,” IEEE Signal Processing Letters, vol. 23, no. 1, p. 98–101 (2015). [Google Scholar]
  14. B. Chen, J. Liang, N. Zheng, and J. C. Principe, “Kernel least mean square with adaptive kernel size,” Neurocomputing, vol. 191, p. 95–106 (2016). [Google Scholar]
  15. R. Fateh and A. Darif, “Mean Square Convergence of Reproducing Kernel for Channel Identification: Application to Bran D Channel Impulse Response,” In : International Conference on Business Intelligence. Springer, Cham, 2021. p. 284–293. [Google Scholar]
  16. ETSI, “Broadband radio acces network (BRAN); hyperlan type 2; physical layer,” Technical report, December 2001. [Google Scholar]
  17. ETSI, “Broadband Radio Acces Network (BRAN); hyperlan type 2; Requirements and archtiectures for wireless broadband access,” Janvier 1999. [Google Scholar]
  18. M. Pouliquen, T. Menard, E. Pigeon, O. Gehan, and A. Goudjil, “Recursive system identification algorithm using binary measurements,” European Control Conference, Aalborg, 2016. [Google Scholar]
  19. N. Aronszajn, “Theory of reproducing kernels,” Transactions of the American mathematical society, vol. 68, no. 3, p. 337–404 (1950). [Google Scholar]

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