Nonlinear Compensation of Carrier Catalytic Methane Sensor Based on Least Squares Support Vector Regression

Detection Principle of carrier catalytic methane sensor is introduced and the nonlinear problem of the sensor is indicated. In order to enhance the measure precision of the methane sensor, the nonlinear compensation model was set up by adopting Least Squares Support Vector Regression which is an Support Vector Machines version that works with a least squares cost function, Support Vector Machines is powerful for the problem characterized by small sample, nonlinearity, and local minima. The kernel of radial basic function was applied in the model. The experimental results show that nonlinear problem of the carrier catalytic methane sensor is greatly compensated by adopting the nonlinear compensation model based on of Least Squares Support Vector Regression, and the model is effective.