Application of Neural Networks for Sensor Validation and Plant Monitoring
- 1 February 1992
- journal article
- research article
- Published by Taylor & Francis in Nuclear Technology
- Vol. 97 (2) , 170-176
- https://doi.org/10.13182/nt92-a34613
Abstract
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 EBRII are presented.Keywords
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