Abstract: 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 a 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.

Key words: Brillouin optical time domain analyzer, whale optimization algorithm, nonlinear convergence factor, artificial neural networks