改进IGABP模型补偿倾角传感器温度漂移研究

Research on temperature drift compensation of inclination sensor by improved IGABP model

倾角传感器很容易受到环境温度变化的影响,产生测量误差,即温度漂移现象。针对此问题,设计了一种基于改进的遗传算法(IGA)优化反向传播神经网络(BPNN)的温度漂移补偿模型。其中遗传算法使用了新的选择策略和交叉变异因子,增加了跳出局部最优解机制。实验结果显示,IGABP补偿模型的均方误差(MSE)为0.00328,经过补偿模型修正后的平均温度漂移为0.039°,远优于未修正时的平均温度漂移0.190°。研究结果表明,IGABP补偿模型与传统的神经网络模型相比,具有更快的收敛速度和更高的补偿精度,能够有效的补偿因温度导致的测量误差,提高倾角传感器的稳定性和精度。

Inclination sensors are susceptible to measurement errors due to ambient temperature changes,namely temperature drift.Aiming at this problem,a temperature drift compensation model based on the improved genetic algorithm(IGA)optimized back propagation neural network(BPNN)was designed.A new selection strategy and crossover mutation operator are used in the genetic algorithm,and a mechanism for jumping out of the local optimal solution is added.The experimental results show that the mean square error(MSE)of the IGABP compensation model is 0.00328,and the average temperature drift after the compensation model correction is 0.039°,which is far better than the average temperature drift of 0.190°without correction.The results show that,the IGABP compensation model has faster convergence speed and higher compensation accuracy compared with the traditional neural network model,which can effectively compensate the measurement error caused by temperature and improve the stability and accuracy of the inclination sensor.