Comparison of back propagation network and radial basis function network in Departure from Nucleate Boiling Ratio (DNBR) calculation

Kerntechnik ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. 15-25
Author(s):  
A. Safavi ◽  
M. H. Esteki ◽  
S. M. Mirvakili ◽  
M. Khaki
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Nucleate Boiling ◽  
Radial Basis ◽  
Propagation Network ◽  
Function Network

Vibration Fault Diagnosis of Rotating Machinery Using PSO-Based Radial Basis Function Network

2013 ◽  
Vol 302 ◽  
pp. 474-480
Author(s):  
Huo Ching Sun ◽  
Chao Ming Huang ◽  
Yann Chang Huang ◽  
Hsing Feng Chen
Keyword(s):  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Pso Algorithm ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Radial Basis ◽  
Function Network

A particle swarm optimization-based radial basis function network (PSO-RBFN) is presented to diagnose vibration faults of steam turbine-generator sets (STGS) in a power plant. The proposed PSO algorithm is used to automatically tune the control parameters of the RBFN. The test results demonstrate that the proposed PSO-RBFN has a higher diagnostic accuracy than the RBFN and multilayer perceptron network (MLPN) trained by error back-propagation algorithm. Moreover, this paper has demonstrated that the proposed PSO-RBFN can be as a reliable tool for vibration fault diagnosis of STGS.

Comparison of back propagation network and radial basis function network in Departure from Nucleate Boiling Ratio (DNBR) calculation

Kerntechnik ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. 15-25
Author(s):  
A. Safavi ◽  
M. H. Esteki ◽  
S. M. Mirvakili ◽  
M. Khaki
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Numerical Solutions ◽  
Back Propagation ◽  
Nucleate Boiling ◽  
Inlet Temperature ◽  
Radial Basis ◽  
Propagation Network

Abstract Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN.

RECOGNITION OF FACIAL EXPRESSION BY IMAGE PROCESSING AND RADIAL BASIS FUNCTION NETWORK

2016 ◽  
Author(s):  
Olímpio Murilo Capeli ◽  
Euvaldo Ferreira Cabral Junior ◽  
Sadao Isotani ◽  
Antonio Roberto Pereira Leite de Albuquerque
Keyword(s):  
Image Processing ◽  
Facial Expression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Radial Basis ◽  
Function Network
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