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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 effectively reduce the measurement error of optical fiber sensor and improve the stability and accuracy of the sensor, this paper presents a neural network optical fiber sensor light intensity compensation and correction method. Firstly, the raw data collected by the sensor is collected and normalized, and then RBF neural network is used for modeling. The quantum particle swarm optimization algorithm is used to optimize the parameters, so that the light intensity compensation and nonlinear correction of the optical fiber displacement sensor are realized. Finally, Experiments are carried out to verify the effectiveness and feasibility of the proposed method. The simulation results show that this method can compensate for the error of the optical fiber sensor and realize the nonlinear correction of the output characteristics of the sensor.