基于BP网络的厚膜压力传感器非线性校正

Non-linear calibration of thick film pressure sensor based on BP neural network

简述了厚膜压力传感器结构原理和力学模型、传统非线性校正方法,以及用神经网络进行非线性校正的原理,探讨用BP神经网络实现厚膜压力传感器的非线性校正,并通过MATLAB神经网络工具箱进行仿真。研究结果表明:采用该方法对弹性体应变量与压力关系的非线性校正可以将标准误差减小2个数量级,简单而有效地实现传感器非线性校正。

The structure and mechanics model of thick film pressare sensor, traditional non-linear calibration＇s methods and the theory of non-linear calibration with artifical neural network （ANN） are introduced. The model of thick-film pressure sensor＇s non-linear calibration with BP neural network is discussed, and the program is emulated by ANN toolbox in MATLAB. The result indicates that the non-linear calibration of elasticity strain and pressure in the toothed can decrease 2 magnitude of the standard error and sensor＇s non-linear calibration is effectively actualized.