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Constructive Morphological Neural Networks: Some Theoretical Aspects and Experimental Results in Classification

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Book cover Constructive Neural Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 258))

Abstract

Morphological neural networks are rooted in mathematical morphology (MM). Several constructive learning algorithms for morphological neural networks have been proposed during the last decade. SinceMMcan be conducted very generally in the complete lattice setting, MNNs are closely related to other lattice-based neurocomputing models.

This paper reviews and analyzes some important types of constructive morphological neural networks including their learning algorithms from the latticetheoretical perspective of mathematical morphology. In particular, we present an improved version of the learning algorithm for the morphological perceptron (MP). Moreover, we incorporate competitive nodes into the two variants of the MP and introduce an approach for training these models. Finally, we compare the performance of several constructive morphological models and of conventional multi-layer perceptrons in some classification problems.

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Authors and Affiliations

  1. Department of Applied Mathematics, IMECC, University of Campinas, Campinas, SP, 13084-970

    Peter Sussner & Estevão Laureano Esmi

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  1. Peter Sussner
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  2. Estevão Laureano Esmi
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Editors and Affiliations

  1. Dept. of Computer Science, University of Malaga , Campus de Teatinos S/N, 29071, Málaga, Spain

    Leonardo Franco  & José M. Jerez  & 

  2. Centre for Computational Intelligence, School of Computing, De Montfort University, The Gateway , LE1 9BH, Leicester, UK

    David A. Elizondo

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Sussner, P., Esmi, E.L. (2009). Constructive Morphological Neural Networks: Some Theoretical Aspects and Experimental Results in Classification. In: Franco, L., Elizondo, D.A., Jerez, J.M. (eds) Constructive Neural Networks. Studies in Computational Intelligence, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04512-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-04512-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04511-0

  • Online ISBN: 978-3-642-04512-7

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