On Kolmogorov's Superposition Theorem and its Applications
A Nonlinear Model for Numerical Function Reconstruction from Discrete Data Sets in Higher Dimensions
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- 192pages
- 7 heures de lecture
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The book introduces a Regularization Network approach utilizing Kolmogorov's superposition theorem to reconstruct higher-dimensional continuous functions from discrete data points. It presents a new constructive proof of the theorem and explores its various versions, linking them to well-known approximation methods and Neural Networks. The work addresses the challenge of the curse of dimensionality, proposing a nonlinear model for function reconstruction within a reproducing kernel Hilbert space. It includes verification and analysis through numerous numerical examples.
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On Kolmogorov's Superposition Theorem and its Applications, Jürgen Braun
- Langue
- Année de publication
- 2010
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- Titre
- On Kolmogorov's Superposition Theorem and its Applications
- Sous-titre
- A Nonlinear Model for Numerical Function Reconstruction from Discrete Data Sets in Higher Dimensions
- Langue
- Anglais
- Auteurs
- Jürgen Braun
- Publié
- 2010
- Format
- souple
- Pages
- 192
- ISBN13
- 9783838116372
- Séries
- Mots clés
- La nature
- Évaluation
- 3 sur 5
- Description
- The book introduces a Regularization Network approach utilizing Kolmogorov's superposition theorem to reconstruct higher-dimensional continuous functions from discrete data points. It presents a new constructive proof of the theorem and explores its various versions, linking them to well-known approximation methods and Neural Networks. The work addresses the challenge of the curse of dimensionality, proposing a nonlinear model for function reconstruction within a reproducing kernel Hilbert space. It includes verification and analysis through numerous numerical examples.