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Nuclear reactor diagnostic system using genetic algorithm (GA)‐trained neural networks
Author(s) -
Chen Yan,
Narita Masakuni,
Yamada Takayoshi
Publication year - 1995
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391150508
Subject(s) - artificial neural network , backpropagation , maxima and minima , genetic algorithm , computer science , nuclear reactor , artificial intelligence , algorithm , pattern recognition (psychology) , machine learning , engineering , mathematics , nuclear engineering , mathematical analysis
Abstract Several nuclear reactor diagnostic systems using neural networks have been proposed in recent years. Neural networks trained by backpropagation, the standard training algorithm, have certain problems such as local minima and long training times. In this paper, neural networks trained by genetic algorithms are used in a nuclear reactor diagnostic system to solve these problems. The system is tested by simulated data modeled on the experimental fast reactor JOYO, and two categories of abnormality (abnormal control rod vibration and abnormal coolant flow) are identified. The comparisons to networks trained by back‐propagation also are discussed.