基于RBF神经网络软测量模型在超声氧浓度计的应用  

Application and Research of Ultrasonic Oxygen Concentration Meter Based on RBF Neural Network Soft Measurement Model

作　　者：郁永斌[1] 和卫星[1] 张翔[1] 汤方剑 

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《仪表技术与传感器》2014年第12期131-133,141,共4页Instrument Technique and Sensor

摘　　要：针对氧浓度信号存在非线性、随机性和易受干扰,难以建立准确测量模型的问题,提出一种RBF神经网络软测量技术应用于超声氧浓度计的方法,该装置测量氧气的温度和超声波在定长管道中氧气传播的时间作为RBF神经网络的输入量进行拟合,采用梯度下降法确定RBF基函数的中心及输出层权值,氧气浓度值作为网络输出量。试验结果表明:采用RBF神经网络曲面拟合所测得氧浓度测量值与顺磁式氧浓度分析仪测量结果绝对误差在1.5%以内,具有一定的工程实用性。As the oxygen concentration signal is non-linear, random and prone to interference, it is difficult to establish an ex- act measurement model. Based on RBF neural network, a method used in the ultrasonic oxygen concentration meter was proposed. We used the equipment to measure the temperature of oxygen and the transmission time of uhrasonic in fixed length pipe and used the results to get the oxygen concentration by RBF neural network. The temperature of oxygen and the time of ultrasonic wave in the oxygen were regarded as the input for RBF neural network and we took the gradient descent method as the output for the network. Experimental results show that the absolute error can be controlled within 1.5% between ultrasonic oxygen concentration meter and paramagnetic oxygen concentration analyzer when usingRBF neural network, and which has certain practical value in the works.

关 键 词：超声波 氧气浓度 径向基函数(RBF) 软测量 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论] TQ123[自动化与计算机技术—计算机科学与技术]