Abstract Neural networks and a symbolic expert system are employed to form a prototype system in BWR anticipated transients without scram (ATWS) accidents diagnosis. Unsupervised learning based on discovery of cluster structures (Pao's approach) and back propagation (BP) neural networks are used to group and memorize different ATWS patterns. Multiple training data sets derived from output files of the SABRE computer code are used in training the BP networks to cope with oscillations of reactor power and other parameters. Tests of neural networks recall correctness are performed, given the presence of time shift of sample data, random noise and incomplete information. The expert system can simulate the ATWS strategy developed by Pennsylvannia Power & Light company. It undertakes diagnosis, using an event tree structure, and can execute the trained BP networks. The expert system is also able to deal with temporary loss of reactor water level information.
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