相关实验视频
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使用神经网络预测基本的轮流轨道直
Chunjiang Liu1, Zhikui Dong2, Xinyu Hou1
1School of Mechanical Engineering, Yanshan University, Hebei Street 438, Qinhuangdao, 066004, China.
Scientific reports
|October 8, 2024
概括
这项研究使用神经网络和有限元模拟来准确预测调整时的开关轨道反弹. 该方法确定了自动轨道直的最佳参数,提高了精度和效率.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 开关轨道反弹在直影响轨道的完整性.
- 准确预测反弹对于有效的轨道直过程至关重要.
研究的目的:
- 分析影响开关轨道反弹的因素.
- 开发一个高精度的神经网络模型来预测这些因素.
- 为了为自动翻转轨道直提供参考.
主要方法:
- 分析影响开关轨道反弹的因素.
- 一个实验组的设计和有限元模拟.
- 用模拟数据训练神经网络,以准确预测.
- 使用U71Mn轨道参数进行验证,并预测所需范围.
主要成果:
- 开发了一个高度准确的神经网络模型.
- 预测和模拟数据之间的误差是最小的.
- 该方法成功地确定了所需结果的参数范围.
结论:
- 开发的神经网络模型准确地预测了交换机轨道反弹.
- 这种方法提供了一个可靠的参考,用于自动直基本转轨道.
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