APPLICATION OF NEURAL NETWORKS FOR SENSOR VALIDATION AND PLANT MONITORING

被引：107

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.

引用

页码：170 / 176

页数：7

共 12 条

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