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Representation of neutron noise data using neural networks

Conference ·
OSTI ID:5660920
; ;
This paper describes a neural network-based method of representing neutron noise spectra using a model developed at the Oak Ridge National Laboratory (ORNL). The backpropagation neural network learned to represent neutron noise data in terms of four descriptors, and the network response matched calculated values to within 3.5 percent. These preliminary results are encouraging, and further research is directed towards the application of neural networks in a diagnostics system for the identification of the causes of changes in structural spectral resonances. This work is part of our current investigation of advanced technologies such as expert systems and neural networks for neutron noise data reduction, analysis, and interpretation. The objective is to improve the state-of-the-art of noise analysis as a diagnostic tool for nuclear power plants and other mechanical systems.
Research Organization:
Oak Ridge National Lab., TN (United States)
Sponsoring Organization:
DOE; USDOE, Washington, DC (United States)
DOE Contract Number:
AC05-84OR21400
OSTI ID:
5660920
Report Number(s):
CONF-920538-24; ON: DE92013232
Country of Publication:
United States
Language:
English

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