基于WOA优化神经网络的BOTDA传感信息提取

BOTDA Sensing Information Extraction Based on Artificial Neural Network Using Whale Optimization Algorithm

人工神经网络(ANN)已被应用于获取布里渊光时域分析仪(BOTDA)所测的布里渊频移信息(BFS),然而其存在易陷入局部最优和收敛速度慢等缺点。为了克服上述缺点,本文提出一种基于WOA优化人工神经网络(WOA-NN)快速获取布里渊光纤传感器BFS的方法;随后通过设计非线性收敛因子α,进一步构建基于非线性WOA优化的神经网络(NWOA-NN)用来提取BFS。将提出的2种网络与经典ANN、粒子群优化神经网络(PSO-NN)、遗传算法优化神经网络(GA-NN)等模型进行比较,实验结果表明,本文所提出的WOA-NN模型在提取BOTDA中的温度信息时的性能优于其他3个网络,其所获取的温度的平均RMSE分别低于ANN、PSO-NN和GA-NN约42.66%、52.51%以及45.93%,NWOA-NN模型所获取的平均RMSE进一步优于WOA-NN 19.08%。同时,使用ANN、PSO-NN、GA-NN、WOA-NN和NWOA-NN进行训练所花费的平均时间分别为929.71 s、889.49 s、699.36 s、580.06 s和549.12 s,所提出的2个网络训练时间表现亦较好。

Artificial neural networks(ANNs) have been employed to acquire Brillouin frequency shift(BFS) information measured by Brillouin optical time domain analyzer(BOTDA),however,it suffers from drawbacks such as easy entrapment in local optima and a slow convergence rate.To overcome the above shortcomings,an artificial neural network using whale optimization algorithm(WOA) for rapid BFS acquisition for Brillouin fiber sensors is proposed in this manuscript.And then a modified nonlinear WOA neural network(NWOA-NN) with a designed nonlinear convergence factor α was put forward to better extract BFS.We compared the proposed networks with ANN,particle swarm optimized neural network(PSO-NN),and genetic algorithm optimized neural network(GA-NN) models.Experimental results show that the performance of the WOA-NN model is better than the latter three,and the average RMSE of temperature obtained by WOA-NN is lower than those of ANN,PSO-NN and GA-NN by approximately 42.66%,52.51% and 45.93%,respectively.The average RMSE by the NWOA-NN model further outperformed the WOA-NN by 19.08%.The average time spent training the ANN,PSO-NN,GA-NN,WOA-NN and NWOA-NN networks were respectively 929.71 s,889.49 s,699.36 s,580.06 s and 549.12 s,our proposed networks illustrated better performance.