Adaptive Population-based Search: Application to Estimation of Nonlinear Regression Parameters
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F07%3AA0800LAP" target="_blank" >RIV/61988987:17310/07:A0800LAP - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Adaptive Population-based Search: Application to Estimation of Nonlinear Regression Parameters
Original language description
This paper deals with algorithms for the estimation of nonlinear regression parameters. Adaptive population-based search algorithms were proposed and implemented for finding reliable estimates at a reasonable time with default setting of their controlling parameters. The algorithms were tested on the NIST collection of datasets containing 27 nonlinear regression tasks of various level of difficulty. The experimental results proved that both our algorithms with competing heuristics are significantly morereliable as compared with the algorithm based on Levenberg-Marquardt optimizing procedure.
Czech name
Adaptivní stochastické algoritmy: aplikace v odhadu parametů nelineárních regresních modélů
Czech description
Článek se zabývá algoritmy pro odhad parametrů v nelineárních regresních modelech. Byly navrženy dva self-adaptivní stochastické algoritmy, které byly na rozsáhlé sadě testovacích úloh výrazně spolehlivější než dosud obvykle užívaný Levenberg-Marquardtůvdeterministický algoritmus při srovnatelné časové náročnosti
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2007
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUT STAT DATA AN
ISSN
0167-9473
e-ISSN
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Volume of the periodical
52
Issue of the periodical within the volume
2
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
12
Pages from-to
713-724
UT code for WoS article
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EID of the result in the Scopus database
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