Kardan M R, Setayeshi S, Koohi-Fayegh R, Ghiassi-Nejad M
Faculty of Physics, Amir Kabir University of Technology, Tehran, Iran.
Radiat Prot Dosimetry. 2003;104(1):27-30. doi: 10.1093/oxfordjournals.rpd.a006158.
The neural network method has been used for the unfolding of neutron spectra in neutron spectrometry by Bonner spheres. A back propagation algorithm was used for training of neural networks. 4 mm x 4 mm bare LiI (Eu) and in a polyethylene sphere set: 2, 3, 4, 5, 6, 7, 8, 10, 12, 18 inch diameter have been used for unfolding of neutron spectra. Neural networks were trained by 199 sets of neutron spectra, which were subdivided into 6, 8, 10, 12, 15 and 20 energy bins and for each of them an appropriate neural network was designed and trained. The validation was performed by the 21 sets of neutron spectra. A neural network with 10 energy bins which had a mean value of error of 6% for dose equivalent estimation of spectra in the validation set showed the best results. The obtained results show that neural networks can be applied as an effective method for unfolding neutron spectra especially when the main target is neutron dosimetry.
神经网络方法已被用于通过邦纳球展开中子能谱法中的中子能谱。采用反向传播算法训练神经网络。使用了直径为2、3、4、5、6、7、8、10、12、18英寸的4毫米×4毫米裸碘化锂（铕）及聚乙烯球套来展开中子能谱。神经网络由199组中子能谱进行训练，这些能谱被细分为6、8、10、12、15和20个能量区间，并为每个区间设计和训练了合适的神经网络。通过21组中子能谱进行验证。在验证集中，对于能谱剂量当量估计误差平均值为6%的具有10个能量区间的神经网络显示出最佳结果。所得结果表明，神经网络可作为展开中子能谱的有效方法，特别是当主要目标是中子剂量测定时。
