优化的RBF神经网络对光纤位移传感器温度补偿

在对光纤位移传感器进行温度标定的过程中，随着工作环境的变化，位移传感器的测量值会产生误差，从而使位移传感器在使用时随着环境温度的变化发生精度下降的情况。为减少这种漂移偏差，该文使用径向基(RBF)神经网络对位移传感器进行温度补偿，并采用自适应的设计思想寻找RBF函数中心。将位移量和环境温度作为输入，其输出为传感器输出电压，使用自适应的设计思想来确定基函数的中心，建立一个基于RBF神经网络的模型。结果表明，该模型的训练结果可以使光纤位移传感器进行测量的相对误差降低9.23%,在测量精度上有很大的改进，证明了该方法的可行性。

Study on Temperature Compensation of Optical Fiber DisplacementSensor Based on Optimized RBF Neural Network

In the process of temperature calibration of optical fiber displacement sensor, it is found that with the change of working environment, the measured value of the displacement sensor will deviate, which will reduce the accuracy of the displacement sensor with the change of ambient temperature. In this paper, the radial basis function (RBF) neural network is used to compensate the temperature of displacement sensor, and a self-adaptive design idea is used to find the center of radial basis function in order to reduce the drift deviation. By taking the displacement and ambient temperature as the input and the sensor output voltage as the output, the adaptive design idea is used to determine the center of the basis function, and a model based on RBF neural network is established. The results show that the training results of the model can reduce the relative error of the optical fiber displacement sensor by 9.23%, and the measurement accuracy is improved greatly, which verifies the feasibility of this method.