基于RBF神经网络算法在重金属浓度测量中的应用

Application of RBF Neural Netwcrk Algorithm in Heavy Metal Concentration Measurement

针对在线重金属检测仪器其测量数据精度较低的问题进行研究,同时考虑电化学传感器随温度变化易产生误差,提出了采用多传感器数据融合方法,将RBF(Radial Basis Function Neural Network)算法应用到检测中。研究结果表明此方法可使重金属浓度检测系统的输出结果更加准确,实现了温度补偿,减轻了环境温度对传感器测量精度和稳定性带来的不利影响。

For low precision of metric data of heavy metal testing instrument,at the same time considering electrochemical sensor with temperature changes are prone to produce error,multisensor data fusion method was presented and RBF（ Radial Basis Function Neural Network） neural network algorithm was applied to the detection. The research result indicates that this method can make the result of heavy metal monitoring system more accurate,thus realizing the temperature compensation and reducing the environmental temperature on the negative influence of sensor measurement accuracy and stability.