Reservoir Computing Networks by Using Gain Saturation in a Semiconductor Optical Amplifier (SOA) Applied to Nonlinear Channel Equalization-Reference-Cited by-同舟云学术

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Reservoir Computing Networks by Using Gain Saturation in a Semiconductor Optical Amplifier (SOA) Applied to Nonlinear Channel Equalization

Published:2024-04-21 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 Photonics & Electromagnetics Research Symposium (PIERS)
language:
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Author:

Liu Xiyong¹,Gao Hanwen²,Luo Huiwen¹,Wen Feng¹

Affiliation:

1. University of Electronic Science and Technology of China,Key Lab of Optical Fiber Sensing and Communications Ministry of Education, School of Information and Communication Engineering,Chengdu,China,611731

2. University of Electronic Science and Technology of China,Glasgow College,Chengdu,China,611731

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10616250/10617814/10618570.pdf?arnumber=10618570

Reference13 articles.

1. Gradient calculations for dynamic recurrent neural networks: a survey

2. The echo state" approach to analysing and training recurrent neural networks-with an erratum note;Jaeger

3. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

4. An experimental unification of reservoir computing methods

5. Information processing using a single dynamical node as complex system

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