Cavity Duplexer Tuning with 1d Resnet-like Neural Networks
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911216976265216 |
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| author | Raskovalov, Anton |
| author_facet | Raskovalov, Anton |
| contents | This paper presents machine learning method for tuning of cavity duplexer with a large amount of adjustment screws. After testing we declined conventional reinforcement learning approach and reformulated our task in the supervised learning setup. The suggested neural network architecture includes 1d ResNet-like backbone and processing of some additional information about S-parameters, like the shape of curve and peaks positions and amplitudes. This neural network with external control algorithm is capable to reach almost the tuned state of the duplexer within 4-5 rotations per screw. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cavity Duplexer Tuning with 1d Resnet-like Neural Networks Raskovalov, Anton Machine Learning Systems and Control This paper presents machine learning method for tuning of cavity duplexer with a large amount of adjustment screws. After testing we declined conventional reinforcement learning approach and reformulated our task in the supervised learning setup. The suggested neural network architecture includes 1d ResNet-like backbone and processing of some additional information about S-parameters, like the shape of curve and peaks positions and amplitudes. This neural network with external control algorithm is capable to reach almost the tuned state of the duplexer within 4-5 rotations per screw. |
| title | Cavity Duplexer Tuning with 1d Resnet-like Neural Networks |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2510.15796 |