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Sensor validation for power plants using adaptive backpropagation neural network

Conference · · IEEE Transactions on Nuclear Science (Institute of Electrical and Electronics Engineers); (USA)
OSTI ID:6605156
;  [1]
  1. Tennessee Univ., Knoxville, TN (USA). Dept. of Nuclear Engineering

Signal validation and process monitoring problems in many cases require the prediction of one or more process variables in a system. The feasibility of using neural networks to characterize one variable as a function of other related variables is studied. The back propagation network (BPN) is used to develop models of signals from both a commercial power plant and the Experimental Breeder Reactor-II (EBR-II). Several innovations are made in the algorithm, the most significant of which is the progressive adjustment of the sigmoidal threshold function and wight updating terms, thus leading to the designation adaptive backpropagation neural network.

OSTI ID:
6605156
Report Number(s):
CONF-900143--
Journal Information:
IEEE Transactions on Nuclear Science (Institute of Electrical and Electronics Engineers); (USA), Journal Name: IEEE Transactions on Nuclear Science (Institute of Electrical and Electronics Engineers); (USA) Vol. 37:2; ISSN 0018-9499; ISSN IETNA
Country of Publication:
United States
Language:
English