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Authors: Pedro Henrique Gouvêa Coelho ; J. F. M. Do Amaral and A. C. S. Tome

Affiliation: State Univ. of Rio de Janeiro, FEN/DETEL, R. S. Francisco Xavier, 524/Sala 5001E, Maracanã, RJ, 20550-900, Brazil

Keyword(s): Neural Networks, Artificial Intelligence Applications, Channel Equalization, Wireless Systems.

Abstract: Artificial Neural Networks have been broadly used in several domains of engineering and typical applications involving signal processing. In this paper a channel equalizer using radial basis function neural networks is proposed, on symbol by symbol basis. The radial basis function neural network is trained by an extended Kalman filter including evolutionary techniques. The key motivation for the equalizer application is the neural network capability to establish complex decision regions that are important for estimating the transmitted symbols appropriately. The neural network training process using evolutionary techniques including an extended Kalman filter enables a fast training for the radio basis function neural network. Simulation results are included comparing the proposed method with traditional ones indicating the suitability of the application.

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Paper citation in several formats:
Coelho, P.; M. Do Amaral, J. and Tome, A. (2020). Radial Basis Function Neural Network Receiver Trained by Kalman Filter Including Evolutionary Techniques. In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-423-7; ISSN 2184-4992, SciTePress, pages 626-631. DOI: 10.5220/0009565806260631

@conference{iceis20,
author={Pedro Henrique Gouvêa Coelho. and J. F. {M. Do Amaral}. and A. C. S. Tome.},
title={Radial Basis Function Neural Network Receiver Trained by Kalman Filter Including Evolutionary Techniques},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2020},
pages={626-631},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009565806260631},
isbn={978-989-758-423-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Radial Basis Function Neural Network Receiver Trained by Kalman Filter Including Evolutionary Techniques
SN - 978-989-758-423-7
IS - 2184-4992
AU - Coelho, P.
AU - M. Do Amaral, J.
AU - Tome, A.
PY - 2020
SP - 626
EP - 631
DO - 10.5220/0009565806260631
PB - SciTePress

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