Critical heat flux function approximation using genetic algorithms

Function approximation is the problem of finding a system that best explains the relationship between input variables and an output variable. We propose two hybrid genetic algorithms (GAs) of parametric and nonparametric models for function approximation. The former GA is a genetic nonlinear Levenbe...

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Vydané v:	IEEE transactions on nuclear science Ročník 52; číslo 2; s. 535 - 545
Hlavní autori:	Kwon, Yung-Keun, Moon, Byung-Ro, Hong, Sung-Deok
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2005 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Approximation Biological cells Correlation Critical heat flux (CHF) Energy research feature extraction Feedforward neural networks Function approximation genetic algorithm (GA) Genetic algorithms Heat flux Heat transfer Input variables Levenberg-Marquardt algorithm Mathematical analysis Mathematical models Neural networks Nuclear fuels Nuclear power generation Nuclear power plants Parametric statistics Studies system identification Testing Uncertainty
ISSN:	0018-9499, 1558-1578
On-line prístup:	Získať plný text
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