Frequency controlled intelligent standalone RF sensor system for dispersive material testing

Abstract

An artificial neural network (ANN) based frequency controlled automated RF sensor system for the characterization of dispersive liquids is presented. The frequency of the designed structure is electronically controlled by varying the applied reverse-biased voltage to the complementary split ring resonator (CSRR) attached varactor diode. A hybrid modelling approach has mainly been devised to account for the effects of biasing circuitry and the parasitic elements. The designed RF sensor can provide a relatively higher tuning bandwidth (800 MHz) for applied DC biasing voltage of 0–25 V. The proposed sensor system employs an artificially intelligent feed-forward neural network architecture, which is trained by the Levenberg–Marquardt training algorithm for the complex permittivity estimation in the designated frequency band with reasonable accuracy. It employs the Bayesian Regularization for better input–output correlation and system performance. The ANN based characterization algorithm is integrated with a graphical user interface (GUI) and implemented using MATLAB.