神经网络在光纤传感器光强补偿及校正中的应用

Application of Neural Network in Optical Fibersensor Intensity Compensation and Correction

为了有效减少光纤传感器的测量误差,提高传感器的稳定性和精度,本文提出一种神经网络的光纤传感器光强补偿及校正方法。首先收集传感器测量原始数据,并进行归一化处理,然后采用RBF神经网络进行建模,并采用量子粒子群算法优化其参数,从而实现光纤位移传感器的光强补偿及非线性校正,最后采用仿真实验以验证本文方法的有效性和可行性,仿真实验结果表明,本文方法可以对光纤传感器误差进行较好补偿,实现了传感器输出特性的非线性校正。

In order to reduce the output error and improve the stability and accuracy of the sensor,a neural network fiber optic sensor is proposed,and the intensity compensation and correction method are proposed. Firstly collect the raw data of sensor output and then normalize RBF neural network modeling optic fiber displacement sensor with intensity compensation and nonlinear correction,finally,test the feasibility and validity of the simulation method,simulation results show that this method can the sensor error for better compensation,but also can realize the output characteristic of sensors nonlinear correction.