Artificial Neural Network in Microwave Cavity Filter Tuning

Michalski, Jerzy Julian; Gulgowski, Jacek; Kacmajor, Tomasz; Mazur, Mateusz

doi:10.1002/9781118405864.ch2

Cited by 2 publications

(1 citation statement)

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“…It seems a fuss to use RL, since other methods, such as training supervised learning models [5,6,14,16,32] or to study the physical characteristics of the cavity filter [3,[10][11][12], can still achieve the same goal; in most cases, those methods also do not need a large number of iterations. However, these methods always need a pretuned filter model as a benchmark or experienced tuning experts for guidance.…”

Section: Discussionmentioning

confidence: 99%

Learning Human Strategies for Tuning Cavity Filters with Continuous Reinforcement Learning

Wang

2022

Applied Sciences

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Learning to master human intentions and behave more humanlike is an ultimate goal for autonomous agents. To achieve that, higher requirements for intelligence are imposed. In this work, we make an effort to study the autonomous learning mechanism to solve complicated human tasks. The tuning task of cavity filters is studied, which is a common task in the communication industry. It is not only time-consuming, but also depends on the knowledge of tuning technicians. We propose an automatic tuning framework for cavity filters based on Deep Deterministic Policy Gradient and design appropriate reward functions to accelerate training. Simulation experiments are carried out to verify the applicability of the algorithm. This method can not only automatically tune the detuned filter from random starting position to meet the design requirements under certain circumstances, but also realize the transfer of learning skills to new situations, to a certain extent.

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