Comparison of artificial intelligence methods in extracting the permittivity of materials with Fabry-Perot open resonator

This paper discusses the application of particular artificial intelligence methods in the process of extracting the permittivity of thin, dielectric materials using Fabry-Perot open resonator (FPOR). The main point of the measurements is presented. Then, the details of the measurement process are explained with highlighting the possible fields of improvement. Several chosen artificial intelligence architectures are shown, including lassic normal networks, multilayer perceptrons and radial-basis networks. For the comparison, there have been also presented Mamdani and Sugeno fuzzy inference systems. There is described the procedure of the measurement, gathering data process and finally the performance is described on the example of medical materials.