Neural networks for sensor validation and plant monitoring

Conference · Mon Jan 01 00:00:00 EST 1990

OSTI ID:7188599

Upadhyaya, B R; Eryurek, E; Mathai, G

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: USDOE; USDOE, Washington, DC (United States)
• DOE Contract Number: FG07-88ER12824
• OSTI ID: 7188599
• Report Number(s): CONF-900804-35; ON: DE93002127
• Resource Relation: Conference: International topical meeting on fast reactor safety, Snowbird, UT (United States), 12-16 Aug 1990
• Country of Publication: United States
• Language: English

Related Subjects

22 GENERAL STUDIES OF NUCLEAR REACTORS
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
EBR-2 REACTOR
MONITORING
NEURAL NETWORKS
ALGORITHMS
COMPUTER NETWORKS
PERFORMANCE
SIGNAL CONDITIONING
TRAINING
BREEDER REACTORS
EDUCATION
EPITHERMAL REACTORS
EXPERIMENTAL REACTORS
FAST REACTORS
FBR TYPE REACTORS
LIQUID METAL COOLED REACTORS
LMFBR TYPE REACTORS
MATHEMATICAL LOGIC
POWER REACTORS
REACTORS
RESEARCH AND TEST REACTORS
SODIUM COOLED REACTORS
220400* - Nuclear Reactor Technology- Control Systems
990200 - Mathematics & Computers