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Building Localized Basis Function Networks Using Context Dependent Clustering

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Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5163))

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Abstract

Networks based on basis set function expansions, such as the Radial Basis Function (RBF), or Separable Basis Function (SBF) networks, have non-linear parameters that are not trivial to optimize. Clustering techniques are frequently used to optimize positions of localized functions. Context-dependent fuzzy clustering techniques improve convergence of parameter optimization, leading to better networks and facilitating formulation of prototype-based logical rules that provide low-complexity models of data.

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Author information

Authors and Affiliations

  1. Electrotechnology Department, Silesian University of Technology, Katowice, Krasiñskiego 8, Poland

    Marcin Blachnik

  2. Department of Informatics, Nicolaus Copernicus University, Grudzidzka 5, Toruñ, Poland

    Wodzisaw Duch

Authors
  1. Marcin Blachnik
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  2. Wodzisaw Duch
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Véra Kůrková Roman Neruda Jan Koutník

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Blachnik, M., Duch, W. (2008). Building Localized Basis Function Networks Using Context Dependent Clustering. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_50

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  • DOI: https://doi.org/10.1007/978-3-540-87536-9_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87535-2

  • Online ISBN: 978-3-540-87536-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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