A gas sensor system coupled to an artificial neural network capable of self-calibration against ambient humidity and temperature fluctuations

The responses of chemoresistive gas sensors suffer from the fluctuations in the background atmospheric conditions. An appropriate countermeasure is required to identify and compensate these drift-like terms introduced in the responses. Here, chemoresistive gas sensor is considered as a non-linear system. This system is characterized and verified by too many experiments. Relative humidity and temperature of the surrounding atmosphere along with the concentration of target gas are considered as inputs of the system while the resistance of the sensor is the output. A MISO model is considered to simulate the behavior of gas sensor. Resistance of the sensor along with the relative humidity and temperature are the inputs of the model. Target gas concentration is the single output of the model. A large database was created out of the experimental results, i.e. the inputs and outputs of the system in different conditions. The model was simulated by the utilization of an appropriate artificial neural network. The sensor and complimentary software created by artificial neural network could exactly predict the gas concentration in presents of drifting parameters like ambient humidity and temperature.