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Dimensionality reduction in kernel-based identification of Wiener system by cyclostationary excitations
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Dimensionality reduction in kernel-based identification of Wiener system by cyclostationary excitations
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Journal Article
Dimensionality reduction in kernel-based identification of Wiener system by cyclostationary excitations
2025
Overview
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.
Publisher
Polish Academy of Sciences
