自归一化神经网络实现EDFA波长相关增益建模的一次性迁移学习
网络与互联网体系结构
2023-10-24 v2 机器学习
摘要
我们提出了一种基于半监督自归一化神经网络的新颖机器学习框架，用于建模多个EDFA的波长相关增益，可实现一次性迁移学习。我们在Open Ireland与COSMOS测试床上的22个EDFA实验表明，即使在跨不同放大器类型工作时，也能实现高精度的迁移学习。
引用
@article{arxiv.2308.02233,
title = {Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain},
author = {Agastya Raj and Zehao Wang and Frank Slyne and Tingjun Chen and Dan Kilper and Marco Ruffini},
journal= {arXiv preprint arXiv:2308.02233},
year = {2023}
}
备注
This paper is a preprint of a paper submitted to ECOC 2023 and is subject to Institution of Engineering and Technology Copyright. If accepted, the copy of record will be available at IET Digital Library