Application of neural networks for sensor validation and plant monitoring
- Tennessee Univ., Knoxville, TN (United States)
Sensor and process monitoring in power plants requires 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 plantwide behavior. The backpropagation (BPN) algorithm has been applied for training multilayer feedforward networks. A new and enhanced BPN algorithm for training neural networks 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 the BPN algorithm. In this paper several results of application to the EBR-II are presented.
- OSTI ID:
- 5855018
- Journal Information:
- Nuclear Technology; (United States), Journal Name: Nuclear Technology; (United States) Vol. 97:2; ISSN NUTYB; ISSN 0029-5450
- Country of Publication:
- United States
- Language:
- English
Similar Records
Neural networks for sensor validation and plant monitoring
Sensor validation for power plants using adaptive backpropagation neural network
Related Subjects
22 GENERAL STUDIES OF NUCLEAR REACTORS
220400* -- Nuclear Reactor Technology-- Control Systems
220600 -- Nuclear Reactor Technology-- Research
Test & Experimental Reactors
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ALGORITHMS
COMPUTERS
DATA
DEC COMPUTERS
EXPERIMENTAL DATA
EXPERT SYSTEMS
INFORMATION
MATHEMATICAL LOGIC
NONLINEAR PROBLEMS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
NUMERICAL DATA
PERFORMANCE
POWER PLANTS
REACTOR MONITORING SYSTEMS
THERMAL POWER PLANTS