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Hybrid Artificial Neural Networks: Models, Algorithms and Data

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Advances in Computational Intelligence (IWANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6692))

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Abstract

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. ANNs are one of the three main components of computational intelligence and, as such, they have been often hybridized from different perspectives. In this paper, a review of some of the main contributions for hybrid ANNs is given, considering three points of views: models, algorithms and data.

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Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Spain

    P. A. Gutiérrez & C. Hervás-Martínez

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  1. P. A. Gutiérrez
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  2. C. Hervás-Martínez
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Editors and Affiliations

  1. Departament d’Enginyeria Electrònica, Universitat Politècnica de Catalunya (UPC), Campus Nord, Edificio C4, c/ Gran Capità s/n, 08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada, C/ Periodista Daniel Saucedo Aranda, 18071, Granada, Spain

    Ignacio Rojas

  3. Departamento Tecnologia Eletrónica, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Gonzalo Joya

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Gutiérrez, P.A., Hervás-Martínez, C. (2011). Hybrid Artificial Neural Networks: Models, Algorithms and Data. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_23

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  • DOI: https://doi.org/10.1007/978-3-642-21498-1_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21497-4

  • Online ISBN: 978-3-642-21498-1

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