Learning and prediction of nuclear radioactive properties with artificial neural networks

In this paper artificial neural networks (ANNs) are trained with nuclear radioactive properties using backpropagation (BP) as the learning paradigm. These properties are the radioactive decay modes and decay-gamma energies. The trained networks are used for predicting these properties for nuclei not included in the training set. The results obtained by prediction on test sets are compared with the actual values. This comparison leads to the conclusion that ANNs may be used for predicting radioactive properties of novel nuclei whose properties may be extremely difficult to calculate theoretically or measure experimentally.<
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