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Record type:	stať ve sborníku (D)
Home Department:	Katedra informatiky a počítačů (31400)
Title:	Robust Algorithm for Estimation of Parameters in Non-linear Regression Model
Citace	Tvrdík, J. Robust Algorithm for Estimation of Parameters in Non-linear Regression Model. In: International conferenceTechnical Computing Prague 2005. Praha: Humusoft, 2005. Humusoft, 2005. s. 124-124. ISBN 80-7080-577-3.

Subtitle
Publication year:	2005
Obor:	Aplikovaná statistika, operační výzkum
Number of pages:	1
Page from:	124
Page to:	124
Form of publication:
ISBN code:	80-7080-577-3
ISSN code:
Proceedings title:	International conferenceTechnical Computing Prague 2005
Proceedings:	Mezinárodní
Publisher name:	Humusoft
Place of publishing:	Praha
Country of Publication:	Sborník vydaný v ČR
Název konference:	Technical Computing Prague 2005
Conference venue:	Praha
Datum zahájení konference:
Typ akce podle státní příslušnosti účastníků:	Evropská akce
WoS code:
EID:

Key words in English:	global optimization; stochastic algorithms; competing heuristics; non-linear regression; NIST datasets; MATLAB
Annotation in original language:	The paper deals with algorithms for estimation of non-linear regression parameters. Stochastic population-based algorithm with competition was implemented and compared with standard gradient algorithm commonly used for least-squares estimates. The results show that this stochastic algorithm found the global minimum in most tasks where gradient algorithm fails. Such population-based algorithms can be used as a tool for estimation of non-linear regression parameters, especially in tasks of higher difficulty level or in tasks when suitable starting values for gradient method are not available.
Annotation in english language:	The paper deals with algorithms for estimation of non-linear regression parameters. Stochastic population-based algorithm with competition was implemented and compared with standard gradient algorithm commonly used for least-squares estimates. The results show that this stochastic algorithm found the global minimum in most tasks where gradient algorithm fails. Such population-based algorithms can be used as a tool for estimation of non-linear regression parameters, especially in tasks of higher difficulty level or in tasks when suitable starting values for gradient method are not available.

References

Reference

R01:	RIV/61988987:17310/05:A1000DFK

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