2007
DOI: 10.1016/j.nucengdes.2006.05.005
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Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

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Cited by 39 publications
(4 citation statements)
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“…Science and Technology of Nuclear Installations was compared against other soft computing methods in neutron noise source reconstruction study [15]. A comparative study was done between RBFN and other multilayer perceptron neural networks in the prediction of critical heat flux [34]. Work by [35] utilized RBFN in identifying Accelerator Driven System based on RBFN and the calculation of departure from nucleate boiling ratio (DNBR) using RBFN [36].…”
Section: Layermentioning
confidence: 99%
“…Science and Technology of Nuclear Installations was compared against other soft computing methods in neutron noise source reconstruction study [15]. A comparative study was done between RBFN and other multilayer perceptron neural networks in the prediction of critical heat flux [34]. Work by [35] utilized RBFN in identifying Accelerator Driven System based on RBFN and the calculation of departure from nucleate boiling ratio (DNBR) using RBFN [36].…”
Section: Layermentioning
confidence: 99%
“…The second layer is a hidden layer of high dimensions which provide a set of radial basis functions used as the processing function of neurons in the hidden layer. The hidden nodes calculate the Euclidean distances between the centers and the network input vector, and pass the results through a radial basis function [12]. The output layer supplies the response of the network to the input.…”
Section: Radial-basis Function Neural Networkmentioning
confidence: 99%
“…Garg et al [2007] applied multilayer perceptron and radial basis function neural networks to predict thermal-hydraulics of natural circulation boilingwater reactor. Finally, Vaziri et al [2007] applied radial basis function and multilayer perceptron neural networks to also predict the critical heat flux. ANN is evidently receiving consideration as a suitable in-reactor analysis tool.…”
Section: A Neural Network In Thermal-hudraulic Prediction (Brief Ovmentioning
confidence: 99%