Hybrid Machine Learning EDFA Model
Shengxiang Zhu, Craig Gutterman, Alan Diaz Montiel, Jiakai Yu, Marco Ruffini, Gil Zussman, Daniel C. Kilper
Abstract
A hybrid machine learning (HML) model combining a-priori and a-posteriori knowledge is implemented and tested, which is shown to reduce the prediction error and training complexity, compared to an analytical or neural network learning model.
Topics & Concepts
Computer scienceA priori and a posterioriArtificial intelligenceMachine learningArtificial neural networkPhilosophyEpistemologyNeural Networks and ApplicationsMachine Learning and ELMCloud Computing and Resource Management