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), Vol. 97:2; ISSN 0029-5450
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
REACTOR MONITORING SYSTEMS
EXPERT SYSTEMS
ALGORITHMS
DEC COMPUTERS
EXPERIMENTAL DATA
NONLINEAR PROBLEMS
NUCLEAR POWER PLANTS
PERFORMANCE
COMPUTERS
DATA
INFORMATION
MATHEMATICAL LOGIC
NUCLEAR FACILITIES
NUMERICAL DATA
POWER PLANTS
THERMAL POWER PLANTS
220400* - Nuclear Reactor Technology- Control Systems
220600 - Nuclear Reactor Technology- Research
Test & Experimental Reactors
990200 - Mathematics & Computers