Exploring the Links between the Fundamental Lemma and Kernel Regression

Fuente: arXiv
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Autores principales: Molodchyk, Oleksii, Faulwasser, Timm
Formato: Preprint
Publicado: 2024
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author Molodchyk, Oleksii
Faulwasser, Timm
author_facet Molodchyk, Oleksii
Faulwasser, Timm
contents Generalizations and variations of the fundamental lemma by Willems et al. are an active topic of recent research. In this note, we explore and formalize the links between kernel regression and some known nonlinear extensions of the fundamental lemma. Applying a transformation to the usual linear equation in Hankel matrices, we arrive at an alternative implicit kernel representation of the system trajectories while keeping the requirements on persistency of excitation. We show that this representation is equivalent to the solution of a specific kernel regression problem. We explore the possible structures of the underlying kernel as well as the system classes to which they correspond.
format Preprint
id arxiv_https___arxiv_org_abs_2403_05368
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Exploring the Links between the Fundamental Lemma and Kernel Regression
Molodchyk, Oleksii
Faulwasser, Timm
Systems and Control
Machine Learning
Optimization and Control
93B30 (Primary) 93C10, 46E22 (Secondary)
Generalizations and variations of the fundamental lemma by Willems et al. are an active topic of recent research. In this note, we explore and formalize the links between kernel regression and some known nonlinear extensions of the fundamental lemma. Applying a transformation to the usual linear equation in Hankel matrices, we arrive at an alternative implicit kernel representation of the system trajectories while keeping the requirements on persistency of excitation. We show that this representation is equivalent to the solution of a specific kernel regression problem. We explore the possible structures of the underlying kernel as well as the system classes to which they correspond.
title Exploring the Links between the Fundamental Lemma and Kernel Regression
topic Systems and Control
Machine Learning
Optimization and Control
93B30 (Primary) 93C10, 46E22 (Secondary)
url https://arxiv.org/abs/2403.05368