在18.5±3 MeV的 (α,n) 反应截面的神经网络预测使用莱文伯格-马奎特算法
Hasan Özdoğan1, Yiğit Ali Üncü2, Mert Şekerci3
1Antalya Bilim University, Vocational School of Health Services, Department of Medical Imaging Techniques, 07190, Antalya, Turkey.
人工神经网络 (ANN) 准确地预测了α-中子 (α,n) 反应截面. 使用Levenberg-Marquardt算法的优化ANN显示出与传统核代码相比更高的预测准确度.
科学领域:
- 核物理 核物理是核物理的.
- 计算物理学的计算物理.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 人工神经网络 (ANN) 越来越多地用于复杂的科学预测.
- 预测反应截面对于核天体物理学和反应堆设计至关重要.
- 准确预测 (α,n) 反应截面对于理解恒星核合成和核废物转化至关重要.
研究的目的:
- 开发和验证一个人工神经网络模型来预测 (α,n) 反应截面.
- 为了优化ANN的性能,使用Levenberg-Marquardt算法.
- 将ANN模型的预测准确度与已建立的核代码 (如TALYS 1.95.5) 进行比较.
主要方法:
- 在实验反应截面数据上训练一个人工神经网络.
- 微调ANN参数使用Levenberg-Marquardt优化算法.
- 验证ANN模型的预测与TALYS 1.95.5的实验数据和理论计算对比.
主要成果:
- 该ANN模型实现了高的相关系数 (R值高达0.98194的验证).
- 与TALYS 1.95 (50,312.74) 相比,ANN模型的平均平方误差 (7620.92) 显著降低.
- 优化的ANN可靠地近似 (α,n) 反应截面.
结论:
- 通过莱文伯格-马奎特算法优化的ANN模型提供了一种可靠和准确的方法来预测 (α,n) 反应截面.
- ANNs提供了一种强大的计算工具,用于推进核物理研究和应用.
- 这种方法显示了对核反应动态的全面调查的巨大潜力.
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