Sensor validation in power plants using adaptive backpropagation neural network
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.
- Research Organization:
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG07-88ER12824
- OSTI ID:
- 6978406
- Report Number(s):
- CONF-900143-39; ON: DE93002959
- Resource Relation:
- Conference: Institute for Electronic and Electrical Engineers (IEEE) nuclear science symposium, San Francisco, CA (United States), 15-19 Jan 1990
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
22 GENERAL STUDIES OF NUCLEAR REACTORS
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
EBR-2 REACTOR
REACTOR MONITORING SYSTEMS
NEURAL NETWORKS
NUCLEAR POWER PLANTS
SIGNAL CONDITIONING
TRAINING
VALIDATION
BREEDER REACTORS
EDUCATION
EPITHERMAL REACTORS
EXPERIMENTAL REACTORS
FAST REACTORS
FBR TYPE REACTORS
LIQUID METAL COOLED REACTORS
LMFBR TYPE REACTORS
NUCLEAR FACILITIES
POWER PLANTS
POWER REACTORS
REACTORS
RESEARCH AND TEST REACTORS
SODIUM COOLED REACTORS
TESTING
THERMAL POWER PLANTS
220600* - Nuclear Reactor Technology- Research
Test & Experimental Reactors
220400 - Nuclear Reactor Technology- Control Systems
990200 - Mathematics & Computers