Cavity Duplexer Tuning with 1d Resnet-like Neural Networks
Machine Learning
2025-10-20 v1 Systems and Control
Systems and Control
Abstract
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.
Keywords
Cite
@article{arxiv.2510.15796,
title = {Cavity Duplexer Tuning with 1d Resnet-like Neural Networks},
author = {Anton Raskovalov},
journal= {arXiv preprint arXiv:2510.15796},
year = {2025}
}