Nuclear reactor diagnostic system using genetic algorithm (GA)‐trained neural networks

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.