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Description
  • Two methods for solving an ill-posed inverse problem based on Fickian diffusion equation and spatio-temporal data from FRAP measurements are presented. The most usual method is the Tikhonov regularization. In our specific problem we have detected difficulties residing in determination of the optimal regularization parameter. Hence, an equivalent method based on least squares with a quadratic constraint regularization is proposed. This latter approach naturally takes into account the noise level in the data and corresponds to the Morozov's discrepancy principle as well.
  • Two methods for solving an ill-posed inverse problem based on Fickian diffusion equation and spatio-temporal data from FRAP measurements are presented. The most usual method is the Tikhonov regularization. In our specific problem we have detected difficulties residing in determination of the optimal regularization parameter. Hence, an equivalent method based on least squares with a quadratic constraint regularization is proposed. This latter approach naturally takes into account the noise level in the data and corresponds to the Morozov's discrepancy principle as well. (en)
Title
  • On the Connection and Equivalence of Two Methods for Solving an Ill-Posed Inverse Problem Based on FRAP Data
  • On the Connection and Equivalence of Two Methods for Solving an Ill-Posed Inverse Problem Based on FRAP Data (en)
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  • On the Connection and Equivalence of Two Methods for Solving an Ill-Posed Inverse Problem Based on FRAP Data
  • On the Connection and Equivalence of Two Methods for Solving an Ill-Posed Inverse Problem Based on FRAP Data (en)
skos:notation
  • RIV/67985807:_____/14:00442759!RIV15-AV0-67985807
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  • 34413
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  • RIV/67985807:_____/14:00442759
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  • inverse problem; parameter identification; Tikhonov regularization; optimization; least squares with a quadratic constraint; L-curve; FRAP (en)
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  • [A986C3E04755]
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  • Valencia
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  • Valencia
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  • Modelling for Engineering and Human Behaviour 2014
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  • Matonoha, Ctirad
  • Papáček, Š.
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  • Universitat Politecnica de Valencia
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  • 978-84-606-5746-0
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