{'fraction_non_alphanumeric': 0.016339869281045753, 'fraction_numerical': 0.0010893246187363835, 'mean_word_length': 5.167785234899329, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Prediction of Power Signal in Nuclear Reactors with Neural Network Based Intelligent Predictors in the Presence of 1/fα Type Sensor Noise

This paper describes the prediction of the power output from the Self-Powered-Neutron-Detector (SPND) in the liquid zone control compartment (LZC) of nuclear reactors, which are important in online global power measurement in a large Pressurized Heavy Water Reactor (PHWR). Noisy measured data from the SPNDs have been smoothened out with the help of an ARMA filter and then the smoothed data is used as the input for the neural networks for training purpose. These typical intelligent predictors have been studied with its variation considering different dynamic neural network structures with integer and fractional order noise considerations in the measured sensor data. The paper reports the best found network structure for the prediction of measured set of noisy SPND data.