Sensor validation in power plants using adaptive backpropagation neural network

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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 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, thus leading to the designation ``Adaptive`` Backpropagation Neural Network. The estimation of system variables is … continued below

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4 p.

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Eryurek, E. & Upadhyaya, B. R. December 31, 1990.

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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 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, thus leading to the designation ``Adaptive`` Backpropagation Neural Network. The estimation of system variables is performed traditionally using either physical models or empirical models. The prediction of system variables is important in control systems for validating instrumentation outputs and for process monitoring. The model-based prediction assumes a fixed structure for characterizing steady-state or dynamic relationship among process variables. The applications to large and complex systems require more time in order to get an accurate model. Since our goal is to relate signals in a subsystem of a plant, such a relationship can be developed by using neural network ``models`` which provide results faster than model-based techniques. Both steady-state and transient behavior can be incorporated into the network during training.

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4 p.

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OSTI; NTIS; GPO Dep.

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  • Institute for Electrical and Electronics Engineers (IEEE) nuclear science symposium,San Francisco, CA (United States),15-19 Jan 1990

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  • Other: DE93002959
  • Report No.: CONF-900143--39
  • Grant Number: FG07-88ER12824
  • Office of Scientific & Technical Information Report Number: 10107647
  • Archival Resource Key: ark:/67531/metadc1275726

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  • December 31, 1990

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  • April 10, 2019, 12:16 p.m.

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Eryurek, E. & Upadhyaya, B. R. Sensor validation in power plants using adaptive backpropagation neural network, article, December 31, 1990; United States. (https://digital.library.unt.edu/ark:/67531/metadc1275726/: accessed May 30, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.

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