在系统漂移下对在线神经网络更新的选择性卡尔曼过方法
Joshua E Hammond1, Tyler A Soderstrom2, Brian A Korgel1,3
1McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton St. Stop C0400, Austin, TX, 78712, USA.
Scientific reports
|December 11, 2025
概括
神经网络模型可以随着时间的推移而漂移. 子集扩展卡尔曼波器 (SEKF) 有效地更新关键参数,提高准确性并降低自适应人工智能系统的计算成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 控制系统工程 控制系统工程
背景情况:
- 神经网络 (NN) 模型的准确性随着现实世界的系统从训练数据中漂移而降低.
- 传统的维护方法,如再培训或微调,在计算上是昂贵的,可能会导致泛化问题.
- 在动态的工业环境中保持NN的性能需要高效和适应性的解决方案.
研究的目的:
- 引入一种在线学习方法,即子集扩展卡尔曼波器 (SEKF),用于自适应神经网络维护.
- 为了实现NN参数的高效实时更新,而无需进行全面的再培训.
- 为了减少与保持模型忠实性相关的计算开销和手工工作.
主要方法:
- SEKF通过训练损失函数的梯度分析来确定影响预测错误的关键NN参数.
- 卡尔曼过是应用到更新只有选定的参数子集的新数据.
- 该方法在合成系统和复杂的工业过程中得到了验证,包括流体催化裂变器.
主要成果:
- 与传统的再培训和微调方法相比,SEKF表现出更高的准确性和效率.
- 观察到每次代计算时间的显著减少.
- 目标参数更新保持了模型忠实性,使用最小的手动干预.
结论:
- 在不断发展的系统中,SEKF为自适应神经网络的维护提供了有效和高效的解决方案.
- 该方法为在工业环境中部署和维护NNs提供了一种实际方法.
- SEKF可实现实时模型调整,保持准确性并降低运营成本.
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