| Summary: | After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper, RBF neural network technology is introduced to fit a nonlinear continuous function to solve the problem of the output error of the sensor being too large due to linear adjustment. Through experimental analysis, it is demonstrated that the RBF neural network model has a higher convergence speed and smaller error than other network models. By embedding the RBF network model into a sensor microcontroller, the error of traditional linear calibration can be reduced by two orders of magnitude and the measurement accuracy of the catalytic sensor can be greatly improved.
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