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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.22502 |
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| _version_ | 1866911027120046080 |
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| author | Anoshin, Matvei Tsurkan, Olga Lopatkin, Vadim Fedichkin, Leonid |
| author_facet | Anoshin, Matvei Tsurkan, Olga Lopatkin, Vadim Fedichkin, Leonid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22502 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stabilization of industrial processes with time series machine learning Anoshin, Matvei Tsurkan, Olga Lopatkin, Vadim Fedichkin, Leonid Machine Learning Systems and Control 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. |
| title | Stabilization of industrial processes with time series machine learning |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2506.22502 |