使用科尔莫戈罗夫-阿诺德网络预测二级大气动荡
概括
一个新的人工神经网络,科尔莫戈罗夫-阿诺德网络 (KAN),使用气象数据准确预测大气动荡. 这项技术通过提高流预测准确度来增强自由空间光通信系统.
科学领域:
- 光学通信是指光学通信.
- 大气物理学 大气物理学
- 人工智能的人工智能
背景情况:
- 自由空间光通信系统容易受到大气流的影响.
- 准确的,高时间分辨率的大气流预测对于优化这些系统至关重要,特别是卫星到地面的激光链接.
研究的目的:
- 引入一种新型的人工神经网络,即科尔莫戈罗夫-阿诺德网络 (KAN),用于预测大气动荡.
- 评估KAN在捕捉气象参数和大气流强度之间的复杂相关性方面的表现.
主要方法:
- 开发了一个新的人工神经网络,科尔摩戈罗夫-阿诺德网络 (KAN),基于科尔摩戈罗夫-阿诺德定理.
- 利用气象参数建立非线性映射,以1秒分辨率预测大气动荡.
- 在KAN中引入可学习的非线性基础函数,以增强对应性捕获.
主要成果:
- KAN在预测大气流强度 (Cn2) 方面表现出有效性,分辨率为1秒.
- 平均绝对百分比误差 (MAPE) 和对称平均绝对百分比误差 (SMAPE) 分别为31.54%和31.44%.
- 通过将MAPE降低6.74%,SMAPE降低4.35%,KAN的表现优于传统的多层感知器 (MLP).
结论:
- 与传统的神经网络相比,科尔摩戈罗夫-阿诺德网络 (KAN) 为大气动荡预测提供了更有效的方法.
- KAN能够在气象数据中建模复杂的相关性,这显著提高了大气折射率结构常数 (Cn2) 预测的准确性.
- 这一进步为提高自由空间光通信系统的可靠性和性能提供了战略支持.
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