The analysis of typical examples shows that the selectivity magnitude of iga can be increased from 3 . 17 to 422 with the aid of this method and consequently gas is detected accurately 以检测甲烷为例,在干扰气体乙烯的体积分数变化了7600 10 ^ ( - 6 )时,经神经网络融合处理后,分析器的选择性系数从3 . 17提高到422 ,主传感器输出的引用误差从58 %降为0 . 65 % ,实现了对甲烷的准确识别。