An approach based on ANFIS and input selection procedure for microwave characterization of dielectric materials

Abstract

PurposeThe principle of microwave characterization of dielectric materials using open-ended coaxial line probe is to link the dielectric properties of the sample under test to the measurements of the probe admittance (Y(f) = G(f)+ jB(f )). The purpose of this paper is to develop an alternative inversion tool able to predict the evolution of the complex permittivity (ε = ε′ – jε″) on a broad band frequency (f from 1 MHz to 1.8 GHz). Design/methodology/approachThe inverse problem is solved using adaptive network based fuzzy inference system (ANFIS) which needs the creation of a database for its learning. Unfortunately, train ANFIS using f, G and B as inputs has given unsatisfying results. Therefore, an inputs selection procedure is used to select the three optimal inputs from new inputs, created mathematically from original ones, using the Jang method. FindingsInversion results of measurements give, after training, in real time the complex permittivity of solid and liquid samples with a very good accuracy which prove the applicability of ANFIS to solve inverse problems in microwave characterization. Originality/valueThe originality of this paper consists on the use of ANFIS with input selection procedure based on the Jang method to solve the inverse problem where the three optimal inputs are selected from 26 new inputs created mathematically from original ones (f, G and B).

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“…Layer 3 contains the nodes which calculates the ratio of the rule's firing strength to all the firing strengths and is normalized. Layer 4 contains the computations of the normalized rules towards the overall output of all node functions [20]. Layer 5 will only have a single node that helps the network compute the overall output as a summation of each rules.…”