通过自适应深层ELM进行轨道角动量的有效识别方法
Haiyang Yu1, Chunyi Chen1, Xiaojuan Hu1
1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究介绍了一种有效的深度学习框架,用于在大气流中识别轨道角动量 (OAM). 新型分析机器学习模型自动适应不同的OAM模式,提高识别精度.
科学领域:
- 光学和光子学 在光学和光子学.
- 机器学习 机器学习
- 大气科学 大气科学
背景情况:
- 在大气流中识别轨道角动量 (OAM) 是一个挑战.
- 现有的方法通常需要人工干预,并与动态环境作斗争.
研究的目的:
- 开发一个自适应的深度学习框架,以获得强大的OAM认可.
- 为了实现不同OAM模式的自动匹配,而无需用户干预.
主要方法:
- 使用衍生式极端学习机器 (ELM) 进行自动OAM模式匹配.
- 使用多层ELM来量化激光点的特征.
- 应用了一个快速代的收缩值算法来进行参数优化和分析表达式导出.
- 开发了激光点特征和OAM模式之间的关系,以实现稳定的神经网络架构.
主要成果:
- 拟议的分析机器学习模型自动匹配OAM模式.
- 该框架成功地从强度分布中提取特征.
- 在实验数据集上的数值模拟显示了OAM识别能力的提高.
结论:
- 开发的深度学习框架为OAM在动荡的大气中识别提供了高效和自适应的解决方案.
- 该方法适用于时间变化的环境,避免试错调整.
- 该方法在OAM识别任务中表现出卓越的表现.
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