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Authors: Marco Ragni and Andreas Klein

Affiliation: Center for Cognitive Science, Germany

ISBN: 978-989-8425-84-3

Keyword(s): Number series, Artificial neural networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Any mathematical pattern can be the generation principle for number series. In contrast to most of the application fields of artificial neural networks (ANN) a successful solution does not only require an approximation of the underlying function but to correctly predict the exact next number. We propose a dynamic learning approach and evaluate our method empirically on number series from the Online Encyclopedia of Integer Sequences. Finally, we investigate research questions about the performance of ANNs, structural properties, and the adequate architecture of the ANN to deal successfully with number series.

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Paper citation in several formats:
Ragni, M. and Klein, A. (2011). SOLVING NUMBER SERIES - Architectural Properties of Successful Artificial Neural Networks.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 224-229. DOI: 10.5220/0003682302240229

@conference{ncta11,
author={Marco Ragni. and Andreas Klein.},
title={SOLVING NUMBER SERIES - Architectural Properties of Successful Artificial Neural Networks},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={224-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003682302240229},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - SOLVING NUMBER SERIES - Architectural Properties of Successful Artificial Neural Networks
SN - 978-989-8425-84-3
AU - Ragni, M.
AU - Klein, A.
PY - 2011
SP - 224
EP - 229
DO - 10.5220/0003682302240229

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