稳定机器学习的动态预测:使用储库计算测试的新型噪声启发的规范化
Alexander Wikner1, Joseph Harvey2, Michelle Girvan1
1Department of Physics, University of Maryland, 4150 Campus Dr, 20742, College Park, United States.
概括
线性多噪音训练 (LMNT) 稳定了混乱系统的机器学习 (ML) 模型. 这种新方法提高了机器学习模型中短期预测和长期气候预测的准确性.
科学领域:
- 动态系统和混沌理论
- 机器学习和人工智能的人工智能
- 计算物理 计算物理
背景情况:
- 机器学习 (ML) 模型可以预测混乱的系统动态.
- 机器学习模型中的反循环可能导致不稳定性和快速错误增长.
- 在训练期间添加噪音是减轻不稳定的已知技术.
研究的目的:
- 为具有内存的ML模型开发一种新的规范化技术.
- 为了确定性地近似训练期间输入噪声的影响.
- 评估这种新技术的有效性,线性多噪声训练 (LMNT),与现有方法相比.
主要方法:
- 在ML模型的损失函数中制定了一个新的惩罚项.
- 开发了线性多噪声训练 (LMNT) 来近似噪声效应.
- 应用LMNT和其他规范化技术用于库拉莫托-西瓦辛斯基方程的储计算模型.
主要成果:
- 通过LMNT和输入噪声规范化,可以无限期地稳定气候预测.
- 这两种方法都产生了气候统计数据,与真正的混乱系统密切相匹配.
- 与其他技术相比,使用LMNT和噪声调节,短期预测的准确性要高得多.
结论:
- 对于混乱系统预测,LMNT有效地稳定了ML模型.
- LMNT的决定性性质允许快速的超参数调整.
- LMNT为混乱动态的准确和稳定的长期预测提供了一个有希望的方法.
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