Robust Ultra-High Resolution Microwave Planar Sensor Using Fuzzy Neural Network Approach

In this paper, we develop a robust and fault-tolerant approach to microwave-based sensitive measurements using fuzzy neural network (FNN). Microwave chemic-identification, recently, is employing active planar ring resonators to enhance the resolutions significantly. However, in practice, when the technology of resolution improves, the results become more prone to minor variations in the measurement setup and user error. In order to eliminate these unwanted and uncontrollable deviations from the final allocations, we propose a novel and robust approach that uses more than one parameter out of measurements and incorporates FNN as a machine learning architecture at the post processing stage of sensing to obtain fault-tolerant classification. We have compared different membership functions used in the FNN and shown improvement in assigning accuracy from 49% (single parameter-dependent) up to 81.5% (three parameters-dependent) on an average of four materials, such as isopropanol-2 (IPA), ethanol, acetone, and water.