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Evolutionary Training of Neuro-fuzzy Patches for Function Approximation

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Artificial Neural Networks — ICANN 2002 (ICANN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2415))

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Abstract

This paper describes how the fundamental principles of GAs can be hybridized with classical optimization techniques for the design of an evolutive algorithm for neuro-fuzzy systems. The proposed algorithm preserves the robustness and global search capabilities of GAs and improves on their performance, adding new capabilities to fine-tune the solutions obtained.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Architecture and Computer Technology, University of Granada, Spain

    Jésus González, Ignacio Rojas, Hector Pomares & Alberto Prieto

  2. Department of Electrical Engineering, Microelectronics University of Dortmund, Germany

    K. Goser

Authors
  1. Jésus González
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  2. Ignacio Rojas
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  3. Hector Pomares
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  4. Alberto Prieto
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  5. K. Goser
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Editor information

Editors and Affiliations

  1. ETS Informática, Universidad Autónoma de Madrid, 28049, Madrid, Spain

    José R. Dorronsoro

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© 2002 Springer-Verlag Berlin Heidelberg

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González, J., Rojas, I., Pomares, H., Prieto, A., Goser, K. (2002). Evolutionary Training of Neuro-fuzzy Patches for Function Approximation. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_91

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  • DOI: https://doi.org/10.1007/3-540-46084-5_91

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44074-1

  • Online ISBN: 978-3-540-46084-8

  • eBook Packages: Springer Book Archive

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