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Memetic Neuro-Fuzzy System with Big-Bang-Big-Crunch Optimisation

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Man–Machine Interactions 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 391))

Abstract

The paper presents a memetic fuzzy inference system based on Big Bang Big Crunch (evolutionary optimisation) and gradient descent (local search) techniques. Tuning parameters of the fuzzy system with evolutionary optimisation failed to be successful, but application of both evolutionary and local optimisation achieved lower error rates than reference system (that uses only gradient descent optimisation). The results of experiments have been statistically verified.

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Krzysztof Siminski

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  1. Krzysztof Siminski
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Correspondence to Krzysztof Siminski .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Brachman

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz CzachĂłrski

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Siminski, K. (2016). Memetic Neuro-Fuzzy System with Big-Bang-Big-Crunch Optimisation. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_50

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  • DOI: https://doi.org/10.1007/978-3-319-23437-3_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23436-6

  • Online ISBN: 978-3-319-23437-3

  • eBook Packages: EngineeringEngineering (R0)

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