虚拟仪器设计中压力传感器的BP神经网络温度非线形校正

BP Neural Network Temperature Nonlinear Errors Correction of Pressure Sensor Based on LabView

压力传感器的输入输出特性大都存在非线性,且易受工作环境温度的影响。利用LabVIEW图形化编程语言,辅以多参量数据采集卡,采用了基于BP神经网络压力传感器非线性校正的模型、算法和实现方法,探讨了虚拟仪器系统中对压力传感器特性进行温度非线性校正。通过计算机仿真与应用,显示了在虚拟仪器系统中使用这种方法不但使压力传感器的性能得到了改善,而且计算时间短,准确度高,有实际应用价值。

The input and output of pressure sensors are almost nonlinear, and they are always influenced by environment. The LabVIEW software and DAQ （data acquisition） of multi-parameters, nonlinear errors correction of pressure sensor based on BP neural network, which includes its model, algorithm and realized techniques, was presented. The general principles of temperature nonlinear correction of virtual instrument system were discussed, too. The results of computer simulations illustrated that the performance of sensors is improved highly. And the construction of the neural network is simple and the precision is good, it has the value to be applied.