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An Approach to ANFIS Performance

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Mendel 2015 (ICSC-MENDEL 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 378))

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Abstract

The paper deals with Adaptive neuro-fuzzy inference system (ANFIS) and its performance. Firstly, ANFIS is described as a hybrid system based on fuzzy logic/sets and artificial neural networks. Subsequently, modifications of ANFIS are proposed. The aim of these modifications is to improve performance, accuracy or reduce computational time. Finally, experiments are presented and findings are assessed.

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Notes

  1. 1.

    Values on both axes are normalized by z-score. So they don’t directly correspond to the values of sinc.

  2. 2.

    OrigAB is used for original ANFIS with A fuzzy sets for x and B for y while ModAB means our modified ANFIS with A fuzzy sets for x and B for y.

  3. 3.

    Accuracy means maximal error computed using E from Eq. (20).

  4. 4.

    Time is measured on our testing machine with Intel Core i5-2540M.

References

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Acknowledgments

This work was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070) and by the Reliability and Security in IT project (FIT-S-14-2486).

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Authors and Affiliations

  1. Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic

    Stepan Dalecky & Frantisek V. Zboril

Authors
  1. Stepan Dalecky
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  2. Frantisek V. Zboril
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Corresponding author

Correspondence to Stepan Dalecky .

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Editors and Affiliations

  1. Faculty of Mechanical Engineering, Department of Applied Computer Science, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Dalecky, S., Zboril, F.V. (2015). An Approach to ANFIS Performance. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-19824-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19823-1

  • Online ISBN: 978-3-319-19824-8

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