# Optimized Feedforward Neural Network Training for Efficient Brillouin Frequency Shift Retrieval in Fiber

## TL;DR

This paper presents an optimized training method for feedforward neural networks to efficiently and accurately retrieve Brillouin frequency shifts in fiber optic sensing, reducing re-training needs and enhancing adaptability.

## Contribution

The authors propose a novel training approach that allows FNNs to be trained once and used across varying experimental conditions, improving efficiency and generalization in BOTDA applications.

## Key findings

- High computational efficiency demonstrated in simulations and experiments
- Single training session suffices for multiple scenarios
- Effective in long-distance fiber sensing (150 km)

## Abstract

Artificial neural networks (ANNs) can be used to replace traditional methods in various fields, making signal processing more efficient and meeting the real-time processing requirements of the Internet of Things (IoT). As a special type of ANN, recently the feedforward neural network (FNN) has been used to replace the time-consuming Lorentzian curve fitting (LCF) method in Brillouin optical time-domain analysis (BOTDA) to retrieve the Brillouin frequency shift (BFS), which could be used as the indicator in temperature/strain sensing, etc. However, FNN needs to be re-trained if the generalization ability is not satisfactory, or the frequency scanning step is changing in the experiment. This is a cumbersome and inefficient process. In this paper, FNN only needs to be trained once with the proposed method. 150.62 km BOTDA is built to verify the performance of the trained FNN. Simulation and experimental results show that the proposed method is promising in BOTDA because of its high computational efficiency and wide adaptability.