基于萤火虫算法优化BP神经网络的光强度补偿方法

The light intensity compensation method is based on the firefly algorithm to optimize the BP neural network

由于非线性因素对光纤位移传感器的影响,造成了传感器测量误差较大的情况。为此提出了一种补偿措施来降低这些非线性因素的影响,使用萤火虫算法(FA)来优化反向传播神经网络(BPNN)混合的算法优化传感器接收到的光功率值。该算法不仅利用了萤火虫算法的寻找粒子群体的最佳位置的搜索性能,还利用了BPNN比较强的局部最优权值阈值搜索性能,最终达到防止BPNN陷入部分样本中优化情况最佳的目的。实验过程中使用两部分传感器接收的光功率值作为数据输入FA-BP算法中进行训练优化,最终与BPNN和粒子群算法优化BP神经网络(PSO-BP)相比,FA-BP算法有收敛精度高和迭代步骤少的优点,可以有效地提升传感器数据的精度和程序的运行速度。

Due to the influence of non-linear factors on the optical fiber displacement sensor,the measurement error of the sensor is relatively large.To this problem,proposes a compensation measure to reduce the impact of these non-linear factors,using the firefly algorithm(FA)to optimize the back propagation neural network(BPNN)hybrid algorithm to improve the optical power value received by the sensor.The algorithm not only uses the search performance of the firefly algorithm to find the best position of the particle population,but also utilize the strong local optimal weight threshold search performance of BPNN,and finally achieves the goal of preventing BPNN from falling into the best optimization situation in some samples.During the experiment,the optical power values received by the two parts of the sensors are exploited as data input into the FA-BP algorithm for training and optimization.Finally,compared with BPNN and particle swarm optimization BP neural network(PSO-BP),the FA-BP algorithm with higher convergence accuracy and few iteration steps can effectively improve the accuracy of sensor data and the running speed of the program.