基于PSO-BP算法的压力传感器温度补偿研究

硅压力传感器在工业环境中使用时,尤其是用于测量油井井下压力的时候,环境温度变化范围通常比较大.硅压力传感器由于其自身结构的原因,输出压力值会呈现非线性变化,大大的降低了压力传感器的测量精度.本文是基于PSO-BP神经网络方法对压力传感器在温度变化时产生的误差进行补偿修正,以达到系统精度要求.PSO-BP算法的本意是使用PSO算法用于对BP神经网络的初始权值和阈值进行改进和筛选,然后再使用BP网络对样本进行训练,以提高系统的泛化能力和稳定性.

The Research of the Pressure Sensor Temperature Compensation Based on PSO-BP Algorithm

When silicon pressure sensor is used in industrial environments,particularly for measuring downhole pressure in deep oil-well,the ambient temperature range is always large.Because of its unique structure,the value of pressure output appears nonlinear changes,greatly reducing the measurement accuracy of pressure sensor.This article is based on PSO-BP neural network method used in pressure sensor compensating and correcting the error when temperature changes to reach the system accuracy requirements.The intention of PSO-BP algorithm is to improve the initial weights and screen the thresholds of BP neural network,then train the samples by using BP neural network in order to improve the generalization ability and stability of system.