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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Veröffentlicht in:	IEEE transactions on nuclear science Jg. 52; H. 2; S. 535 - 545
Hauptverfasser:	Kwon, Yung-Keun, Moon, Byung-Ro, Hong, Sung-Deok
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2005 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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