Vega-Carrillo H R, Hernández-Dávila V M, Manzanares-Acuña E, Mercado G A, Gallego E, Lorente A, Perales-Muñoz W A, Robles-Rodríguez J A
UA de Estudios Nucleares, Universidad Autónoma de Zacatecas, Cuerpo Académico de Radiobiología, Apdo. Postal 336, 98000 Zacatecas, Zac. México.
Radiat Prot Dosimetry. 2006;118(3):251-9. doi: 10.1093/rpd/nci354. Epub 2005 Oct 13.
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.
设计了一种人工神经网络（ANN），仅使用邦纳球谱仪（BSS）的计数率来获取中子剂量。其中包括环境中子剂量、个人中子剂量和有效中子剂量。利用181个中子能谱来计算邦纳计数率和中子剂量。这些能谱从勒让德能谱转换为能量分布，并使用MCNP 4C代码重新划分为31个能量组。重新划分后的能谱、UTA4响应矩阵和注量-剂量系数用于计算BSS中的计数率和剂量。在神经网络训练过程中，将计数率用作输入，将相应的剂量用作输出。训练和测试在MATLAB环境中进行。通过将训练集中使用的BSS计数率修改±5%，研究了BSS计数率的不确定性对用ANN计算的剂量量的影响。在中子剂量学中使用人工神经网络是一种替代方法，克服了与这个病态问题相关的缺点。
