A BiLSTM-based model with an expert fuzzy evaluation feedback loop predicts 1280 seconds of plant parameters after a main steam line break in a simulated CPR1000 reactor, outperforming six baseline models on error and trend-similarity metrics.
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A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants
A BiLSTM-based model with an expert fuzzy evaluation feedback loop predicts 1280 seconds of plant parameters after a main steam line break in a simulated CPR1000 reactor, outperforming six baseline models on error and trend-similarity metrics.