About: Iterated Non-linerar Regression

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	The paper presents iterated algorithm for parameter estimation of non-linear regression model. The non-linear model is firstly approximated by a polynomial. Afterwards, parameter estimation based on measured data is taken as the initial value for the proposed iterated algorithm. As the estimation method, the well-known Least Square Estimation (LSE), artificial neural networks (ANN) or Bayesian methodology (BM) can be used. With respect to the knowledge of initial parameters the measured data are transformed to meet best the non-linear regression criteria (orthogonal data projection). The original and transformed data are used in the next step of the designed iterated algorithm to receive better parameter estimation. The iteration is repeated until the algorithm converges into a final result. The proposed methodology can be applied on all non-linear models that could be approximated by a polynomial function. The illustrative examples show the convergence of the designed iterated algorithm. The paper presents iterated algorithm for parameter estimation of non-linear regression model. The non-linear model is firstly approximated by a polynomial. Afterwards, parameter estimation based on measured data is taken as the initial value for the proposed iterated algorithm. As the estimation method, the well-known Least Square Estimation (LSE), artificial neural networks (ANN) or Bayesian methodology (BM) can be used. With respect to the knowledge of initial parameters the measured data are transformed to meet best the non-linear regression criteria (orthogonal data projection). The original and transformed data are used in the next step of the designed iterated algorithm to receive better parameter estimation. The iteration is repeated until the algorithm converges into a final result. The proposed methodology can be applied on all non-linear models that could be approximated by a polynomial function. The illustrative examples show the convergence of the designed iterated algorithm. (en)
Title	Iterated Non-linerar Regression Iterated Non-linerar Regression (en)
skos:prefLabel	Iterated Non-linerar Regression Iterated Non-linerar Regression (en)
skos:notation	RIV/68407700:21260/14:00225553!RIV15-MSM-21260___
http://linked.open...avai/riv/aktivita	I
http://linked.open...avai/riv/aktivity	I
http://linked.open...iv/cisloPeriodika	4
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Svítek, Miroslav
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta dopravní
http://linked.open...dnocenehoVysledku	23015
http://linked.open...ai/riv/idVysledku	RIV/68407700:21260/14:00225553
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	parameter estimation; adaptive systems; dynamical modeling; iterated estimation; polynomial regression; Least Square Estimation; Bayesian methodology; artificial neural networks (en)
http://linked.open.../riv/klicoveSlovo	adaptive systems Bayesian methodology Least Square Estimation iterated estimation artificial neural networks parameter estimation polynomial regression dynamical modeling
http://linked.open...odStatuVydavatele	CZ - Česká republika
http://linked.open...ontrolniKodProRIV	[B288A172A950]
http://linked.open...i/riv/nazevZdroje	Neural Network World
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	24
http://linked.open...iv/tvurceVysledku	Svítek, Miroslav
http://linked.open...ain/vavai/riv/wos	000341614500006
issn	1210-0552
number of pages	10 (xsd:int)
http://localhost/t...ganizacniJednotka	21260

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