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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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
- Lingua
- Pubblicato
- 2010
- Rilegatura
- (In brossura)
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- Titolo
- On Kolmogorov's Superposition Theorem and its Applications
- Sottotitolo
- A Nonlinear Model for Numerical Function Reconstruction from Discrete Data Sets in Higher Dimensions
- Lingua
- Inglese
- Autori
- Jürgen Braun
- Pubblicato
- 2010
- Formato
- In brossura
- Pagine
- 192
- ISBN13
- 9783838116372
- Serie
- Valutazione
- 3 su 5
- Descrizione
- 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.