论文标题
基于CGAN合奏的不确定性感知替代模型，用于基于离线模型的工业控制问题优化
A cGAN Ensemble-based Uncertainty-aware Surrogate Model for Offline Model-based Optimization in Industrial Control Problems
论文作者
论文摘要
这项研究重点是将基于离线模型的优化应用于现实世界工业控制问题有关的两个重要问题。第一个问题是如何创建一个可靠的概率模型，该模型准确捕获嘈杂的工业数据中存在的动态。第二个问题是如何可靠地优化控制参数，而无需积极从工业系统收集反馈。具体而言，我们介绍了一种新型的CGAN集合合奏感知的替代模型，以用于基于离线模型的工业控制问题的优化。通过对两个代表性案例进行的广泛实验，即离散的控制案例和连续的控制案例，该方法的有效性得到了证明。这些实验的结果表明，我们的方法在基于离线模型的工业控制方面优于几个竞争基线。
This study focuses on two important problems related to applying offline model-based optimization to real-world industrial control problems. The first problem is how to create a reliable probabilistic model that accurately captures the dynamics present in noisy industrial data. The second problem is how to reliably optimize control parameters without actively collecting feedback from industrial systems. Specifically, we introduce a novel cGAN ensemble-based uncertainty-aware surrogate model for reliable offline model-based optimization in industrial control problems. The effectiveness of the proposed method is demonstrated through extensive experiments conducted on two representative cases, namely a discrete control case and a continuous control case. The results of these experiments show that our method outperforms several competitive baselines in the field of offline model-based optimization for industrial control.
