Feed forward neural networks modeling for K-P interactions

被引:84
作者
El-Bakry, MY [1 ]
机构
[1] Fac Educ, Dept Phys, Salalah 211, Oman
关键词
D O I
10.1016/S0960-0779(03)00068-7
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:995 / 1000
页数:6
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