@ARTICLE{Maik_Gabriel_Dimensionality_2025,
 author={Maik, Gabriel and Mzyk, Grzegorz},
 volume={vol. 71},
 number={No 2},
 journal={International Journal of Electronics and Telecommunications},
 pages={361-368},
 howpublished={online},
 year={2025},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={The topic of nonparametric estimation of nonlinear characteristics in the Wiener system is examined. In this regard, the traditional kernel algorithm faces difficulties stemming from the dimensionality associated with the memory length of the dynamic block. A particular class of input sequences has been proposed, which aids in reducing dimensionality and consequently improves the convergence rate of the estimator to the true characteristics. A theoretical analysis of the suggested method is presented.},
 type={Article},
 title={Dimensionality reduction in kernel-based identification of Wiener system by cyclostationary excitations},
 URL={http://ochroma.man.poznan.pl/Content/135231/4_4985_L_Maik_sk_new.pdf},
 doi={10.24425/ijet.2025.153581},
 keywords={Nonlinear systems, Identification, Kernel estimation, Wiener structure, Curse of dimensionality, Cyclostationary signals, Nonparametric regression},
}