http://www.cnr.it/ontology/cnr/individuo/prodotto/ID184907
Piecewise linear approximations of multivariate functions: A multiresolution-based compression algorithm suitable for circuit implementation (Articolo in rivista)
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- Piecewise linear approximations of multivariate functions: A multiresolution-based compression algorithm suitable for circuit implementation (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.apnum.2010.05.002 (literal)
- Alternative label
M. Parodi; M. Gaggero; M. Storace (2010)
Piecewise linear approximations of multivariate functions: A multiresolution-based compression algorithm suitable for circuit implementation
in Applied numerical mathematics; ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, AMSTERDAM (Paesi Bassi)
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- M. Parodi; M. Gaggero; M. Storace (literal)
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- Journal Q2 in Applied Mathematics (literal)
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- 1. Biophysical and Electronic Engineering Department, University of Genoa, Via Opera Pia 11a, Genova, Italy
2. Department of Production Engineering, Thermoenergetics, and Mathematical Models, University of Genoa, P.le Kennedy, Pad. D, Genova, Italy
3. Biophysical and Electronic Engineering Department, University of Genoa, Via Opera Pia 11a, Genova, Italy (literal)
- Titolo
- Piecewise linear approximations of multivariate functions: A multiresolution-based compression algorithm suitable for circuit implementation (literal)
- Abstract
- This paper is concerned with a multiresolution approach to the piecewise-linear approximation of multivariate nonlinear continuous functions. The proposed technique has no restrictions on the number of variables the functions depend on, and is based on the use of piecewise-linear \"hat\" functions. The approximation levels are related to nested function spaces. The multiresolution approach allows one to define a simple model reduction strategy that is based on a proper error definition. The interest in the piecewise-linear approximations and in the hat functions is motivated by the simplicity of their circuit implementations. The efficiency of the method is tested via two benchmark examples, one of which concerns the approximation of the vector field of a nonlinear dynamical system. (literal)
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