University of Bahrain
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Real Time Accidents Diagnosis for Research Reactors using Adaptive Resonance Network

Show simple item record Kandil,Magy Mohamed 2018-07-31T08:43:56Z 2018-07-31T08:43:56Z 2014
dc.identifier.issn 2210-1519
dc.description.abstract Real time locating faults in any nuclear research reactors plants are the highest importance requirements, aimed for safety of human and environmental reasons. Since a great fault can occur in a few milliseconds, accordingly, there is an increasing demand for automated systems to diagnose such failures. Adaptive Resonance network (ART) [1] is a neurofuzzy network, which is an important family of competitive neural learning model. Its memory mode is very similar to that of biological one, and memory capacitycan increase while the learning patterns increase. It can perform real-time online learning, and can work under non stationary world. In this research a new proposed a neural network classifier based on ART, which achieved preferable results than several other neural algorithms will be presented. The proposed algorithm obtains and diagnosis faults accidents patterns in the Multi-Purpose Research Reactor of Egypt, to avoid the risk of occurrence of a nuclear accident. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Artificial Neural Networks (ANN)
dc.subject Adaptive Resonance network (ART)
dc.subject fault diagnosis
dc.subject Nuclear Reactors
dc.subject Multi-Purpose Research Reactor of Egypt
dc.title Real Time Accidents Diagnosis for Research Reactors using Adaptive Resonance Network en_US
dc.type Article en_US
dc.volume 02
dc.issue 03
dc.source.title International Journal of Computing and Network Technology
dc.abbreviatedsourcetitle IJCNT

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