Based on a brief introduction of the key technology of mine methane sensor,this paper mainly deals with the drift reduction of gas sensor,including the zero-adjustment,sensitivity correction and nonlinear compensation of methane sensor and their research progress.Several new techniques were introduced,such as genetic algorithm,wavelet decomposition and the implementation method using DSP.

Especially the sensor′s nonlinear auto-calibration method using neural network was described.The RBF neural network was used as an inverse model that was trained to perform the mapping among the sensor′s readings and the actually sensed properties.

With its converging speed,classification capability and approach capability,the RBF neural network has become a hot research area.

ISweek（http://www.isweek.com/）- Industry sourcing & Wholesale industrial products