ABSTRACT
In this paper, an approach based on neural networks for recognizing the nuclear research reactor accidents
(patterns) is presented. A neural network is designed and trained, initially without noise, to recognize the nuclear
research reactors accidents patterns (using MATLAB's Neural Network Toolbox). When the neural network
response is simulated, the 9x9 simulation output values of the matrix's diagonal is larger than 0.9, (approximately
equal 1), this means the outputs is approximately equal the targets and the network is well trained. A new copy of
the neural network was made, to train it with noisy accident's patterns. When this network was trained on this
noisy input vectors (patterns), it is greatly reduces its errors and its output is approximately equal the output as
when it is trained without noise input vectors. This new copy was trained also on accidents patterns without noise
to gain the maximum performance and the high reliability of the network. Experiments have shown excellent
results; where the network did not make any errors for input vectors (patterns) with noise level from 0.00 up to
0.14. When the noise level larger than 0.15 was added to the vectors (patterns); both neural networks began
making errors
Keywords: - Artificial neural networks (ANN), Nuclear Research Reactor, and MAT