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An artificial neural network (ANN) based tunable automated standalone RF sensor system is proposed to realize an improved sensing module involving a quite accurate solution of the non-linear inverse characterization problem. The presented tunable sensor system is quite novel as it alleviates the need for any active tuning circuitry. Moreover, the proposed unified design topology facilitates a relatively higher tuning range (1900 MHz) than that of the earlier reported (580 MHz) capacitor-based tunable complementary split-ring resonator (CSRR). The higher tuning range of structures resulted from the improved design configuration comprising a modified CSRR design coupled with a modified microstrip line. The obtained dielectric sensitivity is ∼8.8%. The numerically generated S-parameters of various dielectric samples are used here as a training dataset for the ANN, which is trained using the Levenberg–Marquardt backpropagation algorithm in combination with the Bayesian regularization. Finally, several standard test samples at different unloaded tuned frequencies are measured to record the corresponding resonant frequency and magnitude of the S-parameter in order to process them using the proposed ANN-based sensor system. It is found that the developed ANN-based sensor system provides a reasonably accurate value of the extracted complex permittivity over the frequency range under consideration, which basically removes the need for designing multiple resonant structures unlikely to the conventional resonant sensors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]