基于遗传优化的BP神经网络法在甲烷检测中的应用  

Application of BP neural network method based on genetic optimization in methane detection

作　　者：李卯东 梁永直[2] 贾文沛[2] 夏路易[1] 

机构地区：[1]太原理工大学信息工程学院,山西太原030024 [2]太原理工大学机械工程学院,山西太原030024

出　　处：《工矿自动化》2013年第2期51-53,共3页Journal Of Mine Automation

基　　金：山西省社会发展科技攻关计划项目(20120313029-1);山西省回国留学人员科研资助项目(2011-030)

摘　　要：针对传统的最小二乘法拟合红外传感器的输出特性曲线时存在误差大、计算复杂,传统的BP神经网络法拟合红外传感器的输出特性曲线时存在网络收敛速度慢、易陷入局部极小的问题,通过分析改进的最小二乘法和改进的基于遗传优化的BP神经网络法的拟合效果,指出改进的BP神经网络法拟合度较高,并给出了改进的BP神经网络法在甲烷体积分数检测中的实验结果。结果表明,该方法能够拟合出理想的曲线,有效提高了红外传感器的检测精度及响应速度。In view of problems of big error and complex calculation with the least square method and problems of slow network learning speed and being easy to fall into local minimum with traditional BP neural network method when fitting output characteristic curve of infrared sensor,the paper analyzed fitting effect of improved the least square method and improved BP neural network method based on genetic optimization,indicated the improved BP neural network has higher fitting degree and gave experiment result of the improved BP neural network method in detection of methane volume fraction.The result showed that the method can fit ideal curve which improves detection precision and respond speed of infrared sensor.

关 键 词：甲烷检测 红外传感器 输出特性曲线拟合 遗传算法 BP神经网络 最小二乘法 

分 类 号：TD711.3[矿业工程—矿井通风与安全]