Performance Evaluation of Various Training Algorithms for ANN Equalization in Visible Light Communications with an Organic LED

Nazari Chaleshtori, Zahra, Haigh, Paul A., Chvojka, Petr, Zvanovec, Stanislav and Ghassemlooy, Fary (2019) Performance Evaluation of Various Training Algorithms for ANN Equalization in Visible Light Communications with an Organic LED. In: The 2nd West Asian Colloquium on Optical Wireless Communications (WACOWC2019): 27-28 April, Shahid Beheshti University, Tehran, Iran. IEEE, Piscataway, NJ, pp. 11-15. ISBN 9781728137681, 9781728137674

[img]
Preview
Text
wacowc2019-final-19.3.2019.pdf - Accepted Version

Download (1MB) | Preview
Official URL: https://doi.org/10.1109/wacowc.2019.8770203

Abstract

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.

Item Type: Book Section
Uncontrolled Keywords: Artificial neural network equalizer, Equalization, Organic LEDs, Visible light communications
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 29 Jul 2019 11:15
Last Modified: 11 Oct 2019 10:06
URI: http://nrl.northumbria.ac.uk/id/eprint/40180

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence