Intelligent modeling and prediction of nanostructural behavior of humidity sensors

This paper presents the nanostructural characterization of a humidity sensor and obtains its intelligent models by using artificial neural networks and adaptive neuro-fuzzy inference systems. Three major processing components are manipulated via the sol-gel spin-coating technique to prepare an active element to detect humidity variations. The corresponding nanostructural analysis of the humidity sensor using X-ray powder diffraction generated the lattice distortion, known as tetragonality, data. Consequently, the three fabrication input components and the one output component of the humidity sensor data were used to obtain and validate intelligent models of the humidity sensor. The intelligent model and validation results demonstrate that the intelligent models of humidity sensors are suitable to improve the sensor fabrication process, to predict the sensor behavior under various conditions, and to help detect faulty humidity sensors.