Complex lithology prediction using probabilistic neural network improved by continuous restricted Boltzmann machine and particle swarm optimization

2019 ◽  
Vol 179 ◽  
pp. 966-978 ◽  
Author(s):  
Yufeng Gu ◽  
Zhidong Bao ◽  
Xinmin Song ◽  
Shirish Patil ◽  
Kegang Ling
2020 ◽  
Vol 68 (6) ◽  
pp. 1727-1752
Author(s):  
Yufeng Gu ◽  
Zhongmin Zhang ◽  
Demin Zhang ◽  
Yixuan Zhu ◽  
Zhidong Bao ◽  
...  

2021 ◽  
Vol 23 (3) ◽  
pp. 99
Author(s):  
Yoyok Dwi Setyo Pambudi

Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard  probabilistic neural network (PNN)  and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB.  The experiment results showed that supervised PNN PSO could classify severe accident of nuclear power plant better than the standar PNN.


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