用双神经网络提升工业时序过程稳定性，温控效果提升3倍。
Stabilization of industrial processes with time series machine learning
- 构建预测+优化双神经网络管道，替代传统点值优化。
- 温控稳定性提升约3倍，且所需计算资源更少。
- 适合工业过程控制、智能制造领域研究人员参考。
时序过程的稳定是众多工业领域的关键问题。将机器学习应用于该问题可显著提升稳定效果，同时减少计算资源消耗。本文提出一个简单流水线，包含两个神经网络：预言者预测器与优化器。通过将点值优化转化为神经网络训练问题，该方法在温度控制方面相较普通求解器提升了约3倍的稳定性，同时降低计算开销。
原文摘要 · Abstract (English)
The stabilization of time series processes is a crucial problem that is ubiquitous in various industrial fields. The application of machine learning to its solution can have a decisive impact, improving both the quality of the resulting stabilization with less computational resources required. In this work, we present a simple pipeline consisting of two neural networks: the oracle predictor and the optimizer, proposing a substitution of the point-wise values optimization to the problem of the neural network training, which successfully improves stability in terms of the temperature control by about 3 times compared to ordinary solvers.
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