摘要
称重传感器的输入与输出成非线性关系,需进行非线性补偿。文中阐述了称重传感器的非线性误差,并提出了一种基于径向基函数神经网络(RBFNN)的称重传感器非线性误差补偿方法,利用RBFNN构建了称重传感器输入Fx与输出Uox的反函数,实现了称重传感器的非线性误差补偿。实验表明:采用这种方法补偿后,称重传感器大秤量段的非线性相对误差减少了一个数量级,提高了称重准确度。
The relation of load cell＇s input and output is nonlinear,which leads to the weighing error.The factors that nonlinear error of load cell were formulated,and a method for compensating this error based on radial basis function neural network（RBFNN） was proposed.The inverse function of load Fx and load cell＇s output voltage Uox was founded,and then load cell＇s nonlinear error was compensated with this inverse function.The experimental results show that the nonlinear error of load cell with this compensating method based on RBFNN is less than that of no compensating,and its weighing result is more accurate.
出处
《仪表技术与传感器》
CSCD
北大核心
2010年第6期3-5,共3页
Instrument Technique and Sensor
基金
湖南省教育厅优秀青年基金项目(07B042)
关键词
称重传感器
非线性
补偿
径向基函数神经网络
load cell
nonlinear
compensation
radial basis function neural network
