卷积神经网络的应用研究及其在闪电电场波形识别中的优化
Caixia Wang1, Xiaoyi Zhang2, Hui Yang3
1School of Applied Science, Beijing Information Science and Technology University, Beijing, China. caxwangee@bistu.edu.cn.
Scientific reports
|January 13, 2025
概括
这项研究开发了一种卷积神经网络 (CNN) 方法用于闪电波形分类,达到90%以上的准确性. 通过波形适配减少噪声,大大提高了雷电场 (LEF) 分析的识别率.
科学领域:
- 大气物理学 大气物理学
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 准确识别和分类闪电波形对于闪电预测和预警系统至关重要.
- 现有的方法可能受到噪声的限制,需要优化以提高性能.
研究的目的:
- 设计和实施一个卷积神经网络 (CNN) 算法,用于识别和分类闪电电场 (LEF) 波形.
- 从数据集,参数和网络结构的角度优化CNN模型,以提高识别准确性.
- 调查优化策略对模型培训时间和效率的影响.
主要方法:
- 利用北京雷电位置网站的电场观测数据.
- 开发并实施了一种CNN模型用于脉冲信号波形识别.
- 通过调整数据集,模型参数和网络结构来优化CNN网络.
- 研究了各种优化术语及其对训练时间的顺序的影响.
主要成果:
- 通过优化的CNN模型,实现了超过90%的闪电波形识别率.
- 证明噪声预处理,特别是拟合理想化的波形,显著提高识别准确性.
- 发现优化对培训效率有很大影响,培训时间增加了大约51%,优化顺序的影响微不足道.
结论:
- CNN算法非常适合用于分类和识别闪电电场 (LEF) 波形.
- 优化技术对于显著提高识别率至关重要.
- 噪音预处理是提高波形识别系统性能的一个关键步骤.
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