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Proceedings of the Computational Methods in Systems and Software

CoMeSySo 2018: Computational and Statistical Methods in Intelligent Systems pp 225–235Cite as

A Novel Approach of the Approximation by Patterns Using Hybrid RBF NN with Flexible Parameters

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 859))

Abstract

This paper describes a new solution to the optimization task of approximation by radial-basis-function (RBF) neural network. The proposed method is addressed to the problem of variable shape parameters for data using the RBF. It involves the max-min algorithm, the RBF neural network, the algorithm for placing new neuron’s centers and the structure of the future deep learning complex neural network (NN).

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Acknowledgement

The author would like to thank their colleagues at the University of West Bohemia, Plzen, for their discussions and suggestions and anonymous reviewers for their valuable comments and hints provided. The research was supported by projects Czech Science Foundation (GACR) No. 17-05534S and SGS 2016-013.

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Authors and Affiliations

  1. University of West Bohemia, 306 14, Pilsen, Czech Republic

    Mariia Martynova

Authors
  1. Mariia Martynova
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Correspondence to Mariia Martynova .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Zdenka Prokopova

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Martynova, M. (2019). A Novel Approach of the Approximation by Patterns Using Hybrid RBF NN with Flexible Parameters. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational and Statistical Methods in Intelligent Systems. CoMeSySo 2018. Advances in Intelligent Systems and Computing, vol 859. Springer, Cham. https://doi.org/10.1007/978-3-030-00211-4_21

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