APPLICATION OF NEURAL NETWORKS FOR SENSOR VALIDATION AND PLANT MONITORING

被引:108
作者
UPADHYAYA, BR [1 ]
ERYUREK, E [1 ]
机构
[1] OAK RIDGE NATL LAB,OAK RIDGE,TN 37831
关键词
NEURAL NETWORKS; SENSOR VALIDATION; REACTOR MONITORING;
D O I
10.13182/NT92-A34613
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
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. Several results of application to the EBR-II are presented.
引用
收藏
页码:170 / 176
页数:7
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