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- W2128760902 abstract "The patterns recognition of measured quantities for the diagnostic purposes in the field of nuclear research reactors is very important. It represents one of the fundamental tasks for the operation and accidents management. In this paper, the Nuclear Research Reactors accident's pattern recognition is tackled within neural network approach. Such patterns are introduced initially without noise. The simulated output values of the matrix's diagonal are larger than 0.9, (approximately equal 1), this means the outputs is approximately equal the targets and the network is well trained. To increase the reliability of such neural network, the noise ratio up to 50% was added for training in order to ensure the recognition of these patterns if it introduced with noise. Also, because of the limited amount of data (patterns), this work has taken care to increase the size of these data (patterns) when it introduced as training packages, by adding different random noise ratios as different sets at different times to ensure proper training of the neural network components. The neural network has been tested after training, and also finally tested by providing separate data patterns to ensure the ability of the constructed network to recognize these patterns. Experiments have shown excellent results; where the network did not make any errors for input vectors (patterns) with the noise level from 0.00 up to 0.14. When the noise level is larger than 0.15 was added to the input vectors (patterns) both networks began making errors. (Abdelfattah A. Ahmed; Nwal Ahmed Alfishawy; Mohamed A. Albrdini, and Imbaby I. Mahmoud. Nuclear Research Reactors Accidents Pattern Recognition Using Artificial Neural Networks. Journal of American Science 2011;7(4):483-492). (ISSN: 1545-1003). http://www.americanscience.org." @default.
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- W2128760902 date "2011-01-01" @default.
- W2128760902 modified "2023-09-24" @default.
- W2128760902 title "Nuclear Research Reactors Accidents Pattern Recognition Using Artificial Neural Networks" @default.
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