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A Brief Review and Comparison of Feedforward Morphological Neural Networks with Applications to Classification

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Book cover Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5164))

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Abstract

The mathematical background of MNNs can be found in mathematical morphology (MM). Since MM can be conducted very generally in the complete lattice setting, MNNs are closely related to other lattice-based neurocomputing models.

This paper reviews some important types of feedforward morphological neural networks including their mathematical background. In addition, we analyze and compare the performance of feedforward morphological models and conventional multi-layer perceptrons in some classification problems.

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Authors and Affiliations

  1. Institute of Math., Stat. and Sci. Comp, University of Campinas, Campinas, SP, 13081-970,  

    Alexandre Monteiro da Silva & Peter Sussner

Authors
  1. Alexandre Monteiro da Silva
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  2. Peter Sussner
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Véra Kůrková Roman Neruda Jan Koutník

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Monteiro da Silva, A., Sussner, P. (2008). A Brief Review and Comparison of Feedforward Morphological Neural Networks with Applications to Classification. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_81

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  • DOI: https://doi.org/10.1007/978-3-540-87559-8_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87558-1

  • Online ISBN: 978-3-540-87559-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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