Nazari Chaleshtori, Zahra, Haigh, Paul A., Chvojka, Petr, Zvanovec, Stanislav and Ghassemlooy, Zabih (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
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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 |
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| Additional Information: | Funding information: The work is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 764461 (VISION), the Czech Republic funded project GACR 17-17538S, and the UK EPSRC research grant EP/P006280/1: Multifunctional Polymer Light-Emitting Diodes with Visible Light Communications (MARVEL). |
| 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: | 23 Jun 2023 15:15 |
| URI: | https://nrl.northumbria.ac.uk/id/eprint/40180 |
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