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English(EN) Learning Changes the World and Then What Was Learned has Changed: Training and Deploying Neural Networks in the Control Loop

新的分类法详细介绍了闭环控制系统中神经网络的故障模式

本文介绍了一种用于神经网络在闭环控制系统中部署时故障模式的分类法,其中网络的行为会改变其训练时的数据分布。文章详细介绍了诸如闭环识别误差、动作误差和跟踪误差等问题,并解释了离线训练如何因局部损失几何形状导致性能下降。该研究提出了用于时间尺度分离在线适应的设计策略,以缓解这些分布变化,并在非线性Duffing振荡器上进行了验证,为在安全关键型反馈系统中部署机器学习提供了实践指导。 AI

影响 提供了一个框架,用于理解和缓解机器学习模型在安全关键型反馈系统中的部署故障。

排序理由 学术论文,详细介绍了机器学习在控制系统中的新分类法和缓解策略。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的分类法详细介绍了闭环控制系统中神经网络的故障模式

本文如何被排名

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学术论文,详细介绍了机器学习在控制系统中的新分类法和缓解策略。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vittorio Lippi ·

    学习改变世界,然后所学已变:在控制循环中训练和部署神经网络

    arXiv:2610.22084v1 Announce Type: cross Abstract: Neural networks offer critical adaptability for controlling complex systems where nominal modeling proves intractable. However, when offline-trained neural networks are deployed within closed-loop control systems, their actions fu…