用1D ResNet模型快速精准调节腔体双工器的大量调谐螺丝。
Cavity Duplexer Tuning with 1d Resnet-like Neural Networks
- 采用1D ResNet结构结合S参数特征进行监督学习。
- 每颗螺丝仅需4-5次旋转即可接近理想调谐状态。
- 适合需要高精度、少步数调谐的射频系统工程师。
本文提出一种机器学习方法，用于调节具有大量调谐螺丝的腔体双工器。经过测试后，我们放弃传统的强化学习方法，转而采用监督学习框架。所提出的神经网络架构包含1D ResNet-like主干，并融合了关于S参数的附加信息，如曲线形状、峰位和峰值幅度。该神经网络配合外部控制算法，可在每颗螺丝仅4-5次旋转的情况下，几乎达到理想的调谐状态。
原文摘要 · Abstract (English)
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.
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