Stabilization of industrial processes with time series machine learning

Abstract: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.

Machine Learning (cs.LG); Systems and Control (eess.SY); Quantum Physics (quant-ph)

arXiv:2506.22502 [cs.LG]

(or arXiv:2506.22502v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2506.22502 Focus to learn more arXiv-issued DOI via DataCite

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