基于灰色预测理论的瓦斯传感器自校正技术

笔者提出了将灰色预测理论与单片机技术相结合 ,对瓦斯传感器的非线性进行自校正的新思路 ;采用了灰色预测理论中GM(1,1)模型和单片机技术相结合 ,将预测误差值对传感器的实测值进行分段补偿的方法 ,实现对瓦斯传感器的非线性自校正 ;该方法克服了灰色预测理论对波动较大的随机序列的预测精度低的缺陷。其研究结果表明 ,应用该方法得到的预测值与真值的拟合程度好 ,预测值与真值之间最大的差值为 0 .15 0 1% ,而该处校正前的差值为 0 .30 0 0 % ,提高了传感器的测量精度。

Self-correction of Methane Sensor Based on Grey Prediction Theory

Combination of (GM(1, 1)) model of grey prediction theory with SCM technique could get over the limitation of prediction theory providing a new route to realize self-correction of sensors with grey prediction theory. The method would overcome the serious defect of grey prediction theory in predicting random serials with big fluctuation. Test has proven that the predicted values comply well with the real values. The maximum difference between the predicted density and the actual density is (0.150?1%,) while the difference between the measured density and the actual density is (0.300?0%.) The precision is greatly improved.