Neural networks for sensor validation and plant monitoring
Sensor and process monitoring in power plants require the estimation of one or more process variables. Neural network paradigms are suitable for establishing general nonlinear relationships among a set of plant variables. Multiple-input multiple-output autoassociative networks can follow changes in plant-wide behavior. The backpropagation algorithm has been applied for training feedforward networks. A new and enhanced algorithm for training neural networks (BPN) has been developed and implemented in a VAX workstation. Operational data from the Experimental Breeder Reactor-II (EBR-II) have been used to study the performance of BPN. Several results of application to the EBR-II are presented.
- Research Organization:
- Tennessee Univ., Knoxville, TN (United States)
- Sponsoring Organization:
- DOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG07-88ER12824
- OSTI ID:
- 7188599
- Report Number(s):
- CONF-900804-35; ON: DE93002127
- Country of Publication:
- United States
- Language:
- English
Similar Records
Application of neural networks for sensor validation and plant monitoring
Sensor validation for power plants using adaptive backpropagation neural network
Related Subjects
220400* -- Nuclear Reactor Technology-- Control Systems
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ALGORITHMS
BREEDER REACTORS
COMPUTER NETWORKS
EBR-2 REACTOR
EDUCATION
EPITHERMAL REACTORS
EXPERIMENTAL REACTORS
FAST REACTORS
FBR TYPE REACTORS
LIQUID METAL COOLED REACTORS
LMFBR TYPE REACTORS
MATHEMATICAL LOGIC
MONITORING
NEURAL NETWORKS
PERFORMANCE
POWER REACTORS
REACTORS
RESEARCH AND TEST REACTORS
SIGNAL CONDITIONING
SODIUM COOLED REACTORS
TRAINING