基于改进型BP神经网络的SF_6气体传感器  

SF_6 gas sensor based on improved BP neural network

作　　者：陈远鸣[1] 常建华[1,2] 沈婉 裴昱[1] 卞晓阳 

机构地区：[1]南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京210044 [2]南京信息工程大学江苏省气象探测与信息处理重点实验室,南京210044

出　　处：《电子测量与仪器学报》2017年第10期1582-1588,共7页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(11374161);江苏省重点研发计划(BE2016756);江苏高校优势学科Ⅱ期建设工程;江苏省高校品牌专业建设工程;国家级大学生实践创新训练计划(201610300030)资助项目

摘　　要：针对非色散红外SF_6气体传感器测量精度易受环境温度、气压影响的问题,提出采用混合粒子群优化-误差反向传播(PSO-BP)神经网络预测模型对环境温度、气压变化引起的测量偏差进行实时补偿,并与其他补偿方法进行分析比较。实验结果表明,该SF_6气体传感器在气体浓度0~1000×10^(-6)、温度10~40℃、气压100~120 kPa,相对测量误差为1.5%,检测精度小于±15×10^(-6),检测分辨率为1×10^(-6),有效地消除了环境温度、气压波动引起的非线性影响。相比于经验公式法和RBF神经网络补偿方法,该方法具有较高的测量准确度和稳定性,且无需增加电路模块,有利于降低传感器的体积和成本。Aiming at the fact that the measurement accuracy of non-dispersive infrared SFs gas sensor is susceptible to ambient temperature and air pressure, PSO-BP neural network prediction model was used for real-time compensation for the measurement deviation caused by changing ambient temperature and air pressure. This method was then compared with other compensation approaches. The experimental results show that the relative measurement error of this SF6 gas sensor is 1.2%, the measurement accuracy is less than -＋ 15 × 10-6 and the measurement resolution is 1 × 10-6 at the gas concentration of 0 - 1 000 × 10-6, the temperature of 10 -40℃ and the air pressure of 100 - 120 kPa. It could effectively eliminate the nonlinear effect of fluctuating ambient temperature and air pressure. Compared with the empirical formula method and RBF neural network compensation method, this approach has higher measurement accuracy and stability, and dispenses with more circuit control module. So, it can help to reduce the volume and the cost of sensor.

关 键 词：非色散红外 气体传感器 PSO-BP神经网络 实时补偿 

分 类 号：TN215[电子电信—物理电子学]