用神经网络提高SO2传感器的检测速度

制作了新型IDC结构聚苯胺膜SO2传感器，测得不同SO2浓度的电导响应与恢复时间曲线。从这些气敏特性曲线出发，以电导响应曲线的斜率作为网络的输入，对应的SO2浓度作为输出，建立了BP网络预测推理模式，对四组数据的预测结果表明精度较高（误差小于3％），具有很好的预测能力。这种方法不同于传统的标定方法，后者需要4min才能稳定的响应，神经网络样本检测不需要达到稳定的响应就可以预测SO2的浓度，从而大大缩短了结果的响应时间（缩短了75％）。

Enhancement of detection velocity of SO2 sensor by neural network

A kind of new sensor of simple structure, easy manufacture and good performance,IDC struc－ture with polyanline film has been studied, the gas－sensitive characteristics of polyanline film have been tested and the response and comeback curves of current conductance for different SO2 contents have been obtained. A forecasting model of BP neural network starting from gas－sensitive characteristics of polyan－line film with the curve of response current conductance as input nodes of network and the corresponding SO2 concentration as the output of the network has been advanced. By means of the network the four group data have been tested.The results show that the model possess good forecast characteristics with errors smal－ler than 3％.This method is different from traditional calibration, the latter requires stable detection for 4min, while neural network signal pattern detection does not necessarily require a steady response. And as a result, the response time can be greatly reduced (shortened by 75％).