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
来源：
NUCLEAR TECHNOLOGY | 1992年 / 97卷 / 02期
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.
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收藏
页码：170 / 176
页数：7
相关论文
共 12 条
[1]   What Size Net Gives Valid Generalization? [J].
Baum, Eric B. ;
Haussler, David .
NEURAL COMPUTATION, 1989, 1 (01) :151-160
[2]  
Bywater R. L., 1990, Transactions of the American Nuclear Society, V62, P412
[3]  
ERYUREK E, 1989, P IEEE NUCLEAR SCI S
[4]  
KUNG SY, 1987, 1ST P INT C NEUR NET
[5]  
Lippmann R. P., 1988, Computer Architecture News, V16, P7, DOI [10.1109/MASSP.1987.1165576, 10.1145/44571.44572]
[6]  
Narendra K S, 1990, IEEE Trans Neural Netw, V1, P4, DOI 10.1109/72.80202
[7]  
RUMELHART DE, 1987, PARALLEL DISTRIBUTED, V2
[8]  
Shannon C., 1971, MATH THEORY COMMUNIC
[9]  
UPADHYAYA BR, 1989, DOENE3795935 US DEP, V2
[10]  
UPADHYAYA BR, 1989, DOENE3795934 US DEP, V1
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