Evaluation of the performance of various artificial neural networks to the signal fault diagnosis in nuclear reactor systems

Author

Keyvan, Shahla ; Durg, Ajaya ; Rabelo, Luis Carlos

Author_Institution

Dept. of Nucl. Eng., Missouri Univ., Rolla, MI, USA

fYear

1993

fDate

1993

Firstpage

1719

Abstract

A study on the evaluation of the performance and comparison between various paradigms of artificial neural networks in nuclear reactor signal analysis is presented for the purpose of developing a diagnostic monitoring system. Reactor signals from the Experimental Breeder Reactor II (EBR-II) are analysed. The signals are both measured signals collected by a data acquisition system (DAS), as well as simulated signals, ART2, ART2-A, fuzzy adaptive resonance theory (Fuzzy ART), and fuzzy ARTMAP paradigms of the adaptive resonance theory (ART) family, standard backpropagation, cascade correlation, and RCE networks are examined and compared

Keywords

backpropagation; data acquisition; fission reactor core control and monitoring; fission research reactors; fuzzy logic; neural nets; nuclear engineering computing; ART2; ART2-A; EBR-II; Experimental Breeder Reactor II; RCE networks; artificial neural networks; backpropagation; cascade correlation; data acquisition system; diagnostic monitoring system; fuzzy ARTMAP paradigms; fuzzy adaptive resonance theory; nuclear reactor systems; signal fault diagnosis; Artificial neural networks; Backpropagation; Data acquisition; Fuzzy systems; Inductors; Measurement standards; Monitoring; Resonance; Signal analysis; Subspace constraints;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993., IEEE International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-0999-5

Type

conf

DOI

10.1109/ICNN.1993.298816

Filename

298816