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Title: 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-; CODEN: IETNA; TRN: 90-031619
Journal Information:
IEEE Transactions on Nuclear Science (Institute of Electrical and Electronics Engineers); (USA), Vol. 37:2; Conference: Institute for Electronic and Electrical Engineers (IEEE) nuclear science symposium, San Francisco, CA (USA), 15-19 Jan 1990; ISSN 0018-9499
Country of Publication:
United States
Language:
English