open access

https://hdl.handle.net/2115/90541

Modal amplitude and phase estimation of multimode near field patterns based on artificial neural network with the help of grey-wolf-optimizer

英語

Mode scrambler

2021-12

Elsevier

Optical fiber technology

67

102720

A simple and efficient method for estimating modal amplitude and phase of multimode near field patterns (NFPs) based on artificial-neural-network (ANN) with the help of the optimization method is proposed. The inferred amplitude and phase of measured NFPs based on ANN are refined by using a grey-wolf optimizer (GWO). By using the proposed method, the image correlation between reproduced and measured NFPs is improved without re-training of ANN, which is the most time-consuming part of ANN-based numerical modal decomposition technique. Numerical examples of three and six mode cases are presented for the estimation using simple ANN. For six-mode case, the correlation is greatly improved by using the optimizer. Finally, the estimation of the measured NFPs from three-mode exchanger and six-mode mode conversion grating is implemented, and 5% improvement in the correlation value is observed for six-mode case. The proposed method offers alternative way to improve the correlation without using elaborated ANN.

学術雑誌論文

Accepted Manuscript

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

©2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

1068-5200

1095-9912

AA11028390