概括
本研究介绍了一种新型的多阶段极端学习机器 (MSELM),用于预测半导体激光器中的光学混乱,有效处理数据不确定性,以提高性能和减少培训需求.
科学领域:
- 非线性动力学是一种非线性动力学.
- 光学工程是指光学工程.
- 机器学习 机器学习
背景情况:
- 半导体激光器中的光学混乱由于数据不确定性而带来预测挑战.
- 现有的混沌预测方法经常因噪音数据而遭受性能降低.
研究的目的:
- 为半导体激光器中持续光学混乱预测提出一个强大的方法.
- 解决和减轻数据不确定性对预测准确性的影响.
主要方法:
- 开发了一种多阶段极端学习机器 (MSELM) 方法.
- 预测的光强度被用作虚拟训练样本,绕过了对飞行员信号的需求.
- 总最小平方 (TLS) 集成用于参数更新,以有效管理数据不确定性.
主要成果:
- 拟议的MSELM实现了超过4 ns的连续光学混乱预测,正常化平均平方误差 (NMSE) 低于0.012.
- 与现有的基于学习的技术相比,该方法需要的培训样本要少得多.
- 集成TLS通过适当处理数据不确定性,提高了预测长度.
结论:
- 该MSELM提供了一个有效和低开销的解决方案,用于连续光学混乱预测.
- 该方法证明了准确和稳定的预测,即使在时间变化的光学混乱和变化的激光参数.
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