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Performance evaluation of various training algorithms for ann equalization in visible light communications with an organic LED

Lookup NU author(s): Dr Paul HaighORCiD


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© 2019 IEEE.This paper evaluates the effect of training algorithms in an artificial neural network (ANN) equalizer for a feedforward multi-layer perceptron configuration in visible light communication systems using a low bandwidth organic light source. We test the scaled conjugate-gradient, conjugate-gradient backpropagation and Levenberg-Marquardt back propagation (LM) algorithms with 5, 10, 20, 30, and 40 neurons. We show that, LM offers superior bit error rate performance in comparison to other training algorithms based on the mean square error. The training methods can be selected based on the trade-off between complexity and performance.

Publication metadata

Author(s): Nazari Chaleshtori Z, Haigh PA, Chvojka P, Zvanovec S, Ghassemlooy Z

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2nd West Asian Colloquium on Optical Wireless Communications, WACOWC 2019

Year of Conference: 2019

Pages: 11-15

Online publication date: 25/07/2019

Acceptance date: 02/04/2016

Publisher: Institute of Electrical and Electronics Engineers Inc.


DOI: 10.1109/WACOWC.2019.8770203

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728137674