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An Experimental Study of Scenarios for the Agent-Based RBF Network Design

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Intelligent Decision Technologies (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 39))

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Abstract

The paper focuses on the radial basis function neural design problem. Performance of the RBF neural network strongly depends on the network structure and parameters. Making choices with respect to the structure and value of the RBF network parameters involves both stages of its design: initialization and training. The basic question considered in this paper is how these two stages should be carried-out. Should they be carried-out sequentially, in parallel, or perhaps based on other predefined schema or strategy? In the paper an agent-based population learning algorithm is used as a tool for designing of the RBF network. Computational experiment has been planned and executed with a view to investigate effectiveness of different approaches. Experiment results have been analyzed to draw some general conclusions with respect to strategies for the agent-based RBF network design.

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Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Ireneusz Czarnowski & Piotr Jędrzejowicz

Authors
  1. Ireneusz Czarnowski
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  2. Piotr Jędrzejowicz
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Corresponding author

Correspondence to Ireneusz Czarnowski .

Editor information

Editors and Affiliations

  1. FCT, Universidade Nova de Lisboa, Caparica, Portugal

    Rui Neves-Silva

  2. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

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Czarnowski, I., Jędrzejowicz, P. (2015). An Experimental Study of Scenarios for the Agent-Based RBF Network Design. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-19857-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19856-9

  • Online ISBN: 978-3-319-19857-6

  • eBook Packages: EngineeringEngineering (R0)

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