Dimensionality reduction in kernel-based identification of Wiener system by cyclostationary excitations

• Autorzy: Maik Gabriel , Mzyk Grzegorz

Identyfikatory

DOI

10.24425/ijet.2025.153581

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Nonlinear systems; Identification; Kernel estimation; Wiener structure; Curse of dimensionality; Cyclostationary signals; Nonparametric regression

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 2
• Strony: 361--368
• Opis fizyczny: Bibliogr. 27 poz., tab., rys.

Twórcy

autor

Maik Gabriel

• Wrocław University of Science and Technology, Poland

• https://orcid.org/0000-0002-4529-7579

autor

Mzyk Grzegorz

• Wrocław University of Science and Technology, Poland

• https://orcid.org/0000-0002-1701-5095

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).