基于神经网络的双芯谐振光纤色散补偿特性预测分析

Prediction and analysis of dispersion compensation characteristics of a dual-ring resonant fiber based on neural network

本文针对双芯谐振色散补偿微结构光纤中色散调控与结构参数优化等问题,提出了一种基于神经网络的光纤轨道角动量模式色散特性预测方法。通过搭建多层神经网络模型,调节神经网络的隐藏层层数、神经元个数及网络超参数,得到了较为准确的色散预测结果。本文提出的方法建立了光纤结构与色散特性之间的联系与规律,与传统光学仿真计算方法相比,该方法可以更快速高效地找到光纤结构对应的模式色散特性,对光纤结构的设计和优化有一定的指导作用。

Aiming at the problems of dispersion regulation and structural parameter optimization in dual-ring resonant dispersion compensation microstructure fiber, a dispersion characteristic prediction method of orbital angular momentum mode based on neural network is proposed in this paper.By designing a multi-layer neural network model and adjusting the number of hidden layers, neurons and super parameters of the neural network, accurate dispersion prediction results are obtained.This proposed method establishes the relationship between fiber structures and dispersion characteristics.Compared with the traditional optical simulation method, this method can calculate the dispersion characteristics for different fiber structures more quickly and efficiently, which could provide a new approach for the design and optimization of fiber structures.