Neuro-fuzzy approach in microwave filter tuning

Learning Human Strategies for Tuning Cavity Filters with Continuous Reinforcement Learning

“…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.…”

“…Similarly, Zhou et al exploited Support Vector Regression (SVR) to construct metamodels between screw rotations and coupling matrix change [6,14]; the screw rotations are not measured but obtained from two optimization procedures, and the calculation of coupling matrix also brings uncertainties. Furthermore, fuzzy logic control [7,15], neural-fuzzy control [16], Extreme Learning Machine (ELM) [17], and regularized deep belief network (R-DBN) [18] have also been applied in modeling cavity filters. These data-driven methods learn models simply from pregathered data without analyzing the complex theoretical and physical characteristics of the filter product, eliminating the error produced by the difference between the ideal model and the real product.…”