Regularization networks for inverse problems: A state-space approach
type: Item
instance of: scholarly article
title: Regularization networks for inverse problems: A state-space approach
zbMATH Open document ID: 1024.93056
DOI: 10.1016/S0005-1098(02)00280-7
published in: Automatica
publication date: 2003-04-27T00:00:00Z
review text: The solution of inverse problems through the regularization method yields a regularization network. Once the activation functions are known, the inverse problem can then be solved without any form of numerical integration. By exploiting the state-space representation and the Bayesian interpretation of regularization, it is shown how to compute the activation functions of the regularization network as well as the matrices needed to compute the weights, which can be computed in \(O(N)\) operations through Kalman filtering techniques. Finally, the algorithms are successfully applied to a real-world problem arising in the study of glandular hormone secretion.
reviewed by: A. M. Kovalev
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 65L09
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 1901007
zbMATH Keywords: inverse problem
zbMATH Keywords: neural networks
zbMATH Keywords: regularization
zbMATH Keywords: Bayesian estimation deconvolution
zbMATH Keywords: Kalman filter
author: Giuseppe De Nicolao
author: Giancarlo Ferrari-Trecate
MaRDI profile type: Publication
cites work: Q4010408
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description: scientific article; zbMATH DE number 1901007