Sensor validation for power plants using adaptive backpropagation neural network

Author

Eryurek, E. ; Upadhyaya, B.R.

Author_Institution

Dept. of Nucl. Eng., Tennessee Univ., Knoxville, TN, USA

Volume

37

Issue

2

fYear

1990

fDate

4/1/1990 12:00:00 AM

Firstpage

1040

Lastpage

1047

Abstract

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 backpropagation 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 weight updating terms

Keywords

adaptive systems; fission reactor instrumentation; neural nets; nuclear engineering computing; nuclear power stations; EBR-II; Experimental Breeder Reactor-II; adaptive backpropagation neural network; commercial power plant; power plants; process monitoring problems; sigmoidal threshold function; weight updating terms; Acceleration; Adaptive systems; Artificial neural networks; Backpropagation algorithms; Equations; Monitoring; Multi-layer neural network; Neural networks; Power generation; Steady-state;

fLanguage

English

Journal_Title

Nuclear Science, IEEE Transactions on

Publisher

ieee

ISSN

0018-9499

Type

jour

DOI

10.1109/23.106752

Filename

106752