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Authors: Branimir Todorović 1 ; Miomir Stanković 1 and Claudio Moraga 2

Affiliations: 1 University of Niš and Technical University of Dortmund, Serbia ; 2 European Centre for Soft Computing and Technical University of Dortmund, Spain

Keyword(s): Recurrent Neural Networks, Bayesian Estimation, Nonlinear Derivative Free Estimation, Chaotic Time Series Prediction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The problem of recurrent neural network training is considered here as an approximate joint Bayesian estimation of the neuron outputs and unknown synaptic weights. We have implemented recursive estimators using nonlinear derivative free approximation of neural network dynamics. The computational efficiency and performances of proposed algorithms as training algorithms for different recurrent neural network architectures are compared on the problem of long term, chaotic time series prediction.

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Paper citation in several formats:
Todorović, B.; Stanković, M. and Moraga, C. (2014). Derivative Free Training of Recurrent Neural Networks - A Comparison of Algorithms and Architectures. In Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2014) - NCTA; ISBN 978-989-758-054-3, SciTePress, pages 76-84. DOI: 10.5220/0005081900760084

@conference{ncta14,
author={Branimir Todorović. and Miomir Stanković. and Claudio Moraga.},
title={Derivative Free Training of Recurrent Neural Networks - A Comparison of Algorithms and Architectures},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2014) - NCTA},
year={2014},
pages={76-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005081900760084},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2014) - NCTA
TI - Derivative Free Training of Recurrent Neural Networks - A Comparison of Algorithms and Architectures
SN - 978-989-758-054-3
AU - Todorović, B.
AU - Stanković, M.
AU - Moraga, C.
PY - 2014
SP - 76
EP - 84
DO - 10.5220/0005081900760084
PB - SciTePress

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