Neural network representation for electron and positron collisions with sodium and potassium atoms

被引：0
|
作者：
El-Bakry, SY
El-Bakry, MY
机构：
[1] King Abdulaziz Univ Branch, Fac Sci, Dept Phys, Madinah Munawwarah, Saudi Arabia
[2] Ain Shams Univ, Fac Educ, Dept Phys, Cairo, Egypt
来源：
INDIAN JOURNAL OF PHYSICS | 2004年 / 78卷 / 12期
关键词：
neural network; electron and positron collisions; sodium and potassium atoms; total collisional cross sections;
D O I：
暂无
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
An artificial neural network (ANN) has been designed to simulate the collisions of electrons and positrons with sodium and potassium atoms at low and intermediate energies. The ANN model has been trained based on experimental data to produce the total cross sections for each case. Levenberg-Marquardt algorithm (LMA) has been employed to train the ANN. The experimental, trained and calculated total collisional cross sections are compared. The designed ANN shows a good match to the experimental data.
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页码：1313 / 1318
页数：6
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