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基于meta学习的剩余网络,用于在有限的数据的情况下预测工业生产质量
Yiguan Shi1,2, Yazhao Cao3, Yong Chen4
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Scientific reports
|May 25, 2024
概括
本研究介绍了一种基于残余网络 (MLRN) 模型的新型超级学习,用于有效预测生产质量,即使使用有限的工业数据. 通过使用MLRN方法,可以增强深度学习模型,以克服数据稀缺性和防止数据退化.
科学领域:
- 工业工程 工业工程 工业工程
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 用于生产质量预测的深度学习模型在有限的真实世界工业数据中扎.
- 在深度学习网络中,训练期间的模型退化是一个重大挑战.
研究的目的:
- 提出基于剩余网络 (MLRN) 的超级学习模型,以有限的数据进行生产质量预测.
- 在深度学习培训过程中解决模型退化问题.
主要方法:
- 开发了一个MLRN模型,训练了各种学习任务,以获得预测知识.
- 使用了带有有效通道注意力 (ECA) 的增强剩余网络来提取特征并减轻梯度问题.
- 实施了多批次和多任务数据输入策略,以防止过度拟合.
主要成果:
- 在生产质量预测中,MLRN模型证明了可用性和有效性.
- 通过对数字和图形数据集的其他模型进行比较来验证性能.
结论:
- 拟议的MLRN模型为在数据稀缺的工业环境中生产质量预测提供了可行的解决方案.
- 整合元学习,增强的残留网络和注意力机制提高了模型的稳定性和预测准确性.
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