Artificial neural networks in neutron dosimetry.
An artificial neural network (ANN) has been designed to obtain neutron doses using only the count rates of a Bonner spheres spectrometer (BSS). Ambient, personal and effective neutron doses were included. One hundred and eighty-one neutron spectra were utilised to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in the BSS and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing were carried out in the MATLAB environment. The impact of uncertainties in BSS count rates upon the dose quantities calculated with the ANN was investigated by modifying by +/-5% the BSS count rates used in the training set. The use of ANNs in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem.
Vega-Carrillo
H R
HR
UA de Estudios Nucleares, Universidad Autónoma de Zacatecas, Cuerpo Académico de Radiobiología, Apdo. Postal 336, 98000 Zacatecas, Zac. México. fermineutron@yahoo.com
Hernández-Dávila
V M
VM
Manzanares-Acuña
E
E
Mercado
G A
GA
Gallego
E
E
Lorente
A
A
Perales-Muñoz
W A
WA
Robles-Rodríguez
J A
JA
D000465:Algorithms; D001822:Body Burden; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009502:Neutrons; D011829:Radiation Dosage; D011834:Radiation Monitoring / Q000295:instrumentation / Q000379:methods*; D011835:Radiation Protection / Q000379:methods*; D012062:Relative Biological Effectiveness; D013057:Spectrum Analysis / Q000295:instrumentation / Q000379:methods*