Fuzzy Nonuniform Sampling for Inverse Decision-Making Modeling to Tune Microwave Filters

Abstract

Microwave filters (MFs) are indispensable in communication systems for selecting specific frequency signals. The tuning of MFs is a demanding and time-consuming task, which can be addressed by the inverse decision-making model (IDMM). However, two main challenges arise in the sampling process for IDMM, namely, low efficiency due to the large number of samples and poor adaptability in the presence of uncertain initial positions. To overcome these challenges, a fuzzy nonuniform sampling (FNUS) method is proposed, leveraging the flexibility of the fuzzy logic system. Specifically, an adaptive sampling framework based on a fuzzy logic system is presented to handle the uncertainty of initial positions. Under this framework, a nonuniform sampling approach is devised to collect fewer samples far from the target and more samples close to the target. Given the similarity and single-sided distribution of samples in the raw dataset oriented to modeling, the tailored enhancement strategies are designed to improve dataset quality. Finally, the efficiency and adaptability of FNUS are demonstrated to be superior to the existing methods through simulations. Furthermore, the practicality of FNUS is validated by experiments on physical MFs.