为了实现光纤位移传感器的光强补偿和减小测量误差, 提出了一种基于遗传算法(GA)优化BP神经网络的光强补偿及校正模型。首先通过对光纤位移传感器做标定实验, 获得传感器测量的原始数据, 然后采用GA-BP神经网络进行建模, 通过对遗传算法的适应度函数、编码方式和参数进行研究, 利用遗传算法的全局寻优能力对传统BP神经网络的权值、阈值进行优化, 改善了其容易陷入局部极值的问题。最后利用实测数据对GA-BP网络和传统BP网络进行训练, 实验结果表明, GA-BP网络比BP网络的预测误差小很多, 提高了补偿精度, 从而实现了光纤位移传感器的光强补偿。

In order to achieve light intensity compensation and reduce measurement error of fiber displacement sensor,a model of light intensity compensation and correction was proposed based on BP neural network optimized by Genetic Algorithm (GA).First,through the calibration experiment to the optical fiber displacement sensor,the original data was obtained.Then,the GA-BP neural network was used for modeling.Through the study on the encoding method,fitness function and parameters of GA,the global optimization capability of GA was used to optimize the weights and thresholds of traditional BP neural network,which made it less easier to fall into local extreme.Finally,the measured data was used to train the GA-BP network and the traditional BP network.The experimental results show that:compared with BP network,the GA-BP network has much smaller prediction error and higher compensation accuracy,and thus can realize the intensity compensation of the optical fiber displacement sensor.