Martinez Blanco, María del Rosario; Ornelas Vargas, Gerardo; Castañeda Miranda, Celina Lizeth; Solís Sánchez, Luis Octavio; Castañeda Miranda, Rodrígo; Vega Carrillo, Héctor René; Celaya Padilla, José María; Garza Veloz, Idalia; Martínez Fierro, Margarita de la Luz; Ortíz Rodríguez, José Manuel
Resumen:
The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral
information is not simple because the unknown is not given directly as a result of the measurements.
Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem,
however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the
network topology and the long training time. Compared to BPNN, it's usually much faster to train a
generalized regression neural network (GRNN). That's mainly because spread constant is the only
parameter used in GRNN. Another feature is that the network will converge to a global minimum,
provided that the optimal values of spread has been determined and that the dataset adequately represents
the problem space. In addition, GRNN are often more accurate than BPNN in the prediction.
These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work
presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation
of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner
spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on
a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International
Atomic Energy Agency compilation.