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International Joint Conference on Rough Sets

IJCRS 2017: Rough Sets pp 199–206Cite as

A Framework for Analysis of Granular Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Abstract

Granular neural networks are neural networks which operate at the level of information granules. Granules, in turn, can be seen as collections of objects that exhibit similar structure or possess similar functionality. In this work we try to provide a comprehensive look at the problem of how granular, feed-forward neural networks conduct their computations, i.e. what is the interpretation for the connections and the neurons of such networks. The paper orbits around the assumption that the networks come from the superposition of their certain subnetworks which emulate membership functions for the granules. The superposition represents an aggregation of a certain number of granules into another one. This interpretation comes from a general granular tree-model that is constructed prior to the network and which describes a particular problem in a semantically understandable form.

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Author information

Authors and Affiliations

  1. University of Warsaw, Warsaw, Poland

    Julian Skirzyński

Authors
  1. Julian Skirzyński
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Correspondence to Julian Skirzyński .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Skirzyński, J. (2017). A Framework for Analysis of Granular Neural Networks. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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