Gene Expression Programming in Sensor Characterization: Numerical Results and Experimental Validation

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Abstract
In this paper, impedance spectroscopy, gene expression programming (GEP), and genetic algorithms are combined to perform sensor characterization. The process presented is useful when there is no knowledge of the sensor equivalent circuit, and a set of impedance responses can be obtained for different measurand values. These responses are used by the algorithm to determine a suitable equivalent circuit and choose a circuit component that describes the measurand values. From this component, interpolation is used to infer the measurand value from the measured frequency responses. Improvements on the application of GEP to impedance characterization are presented. The method is validated through its application to numerical results of a humidity sensor and measurement results of a viscosity sensor.

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