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Optimization in Fuzzy Flip-Flop Neural Networks

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Computational Intelligence in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 313))

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Abstract

The fuzzy J-K and D flip-flops present s-shape transfer characteristics in same particular cases. We propose the fuzzy flip-flop neurons; single input-single output units derived from fuzzy flip-flops as sigmoid function generators. The fuzzy neurons-based neural networks, Fuzzy Flip-Flop Neural Networks (FNN) parameters are quasi optimized using a second-order gradient algorithm, the Levenberg-Marquardt method (LM) and an evolutionary algorithm, the Bacterial Memetic Algorithm with Modified Operator Execution Order (BMAM). The quasi optimized FNN’s performance based on Dombi and Yager fuzzy operations has been examined with a series of test functions.

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Author information

Authors and Affiliations

  1. Inst. of Informatics, Electrical and Mechanical Engineering, Faculty of Engineering Sciences, Széchenyi István University, Hungary

    Rita Lovassy, László T. Kóczy & László Gál

  2. Inst. of Microelectronics and Technology, Kandó Kálmán Faculty of Electrical Engineering, Óbuda University, Budapest, Hungary

    Rita Lovassy

  3. Dept. of Telecommunication and Media Informatics, Budapest University of Technology and Economics, Hungary

    László T. Kóczy

  4. Dept. of Technology, Informatics and Economy University of West Hungary,  

    László Gál

Authors
  1. Rita Lovassy
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  2. László T. Kóczy
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  3. László Gál
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Editor information

Editors and Affiliations

  1. Óbuda University, Bécsi út 96/B, H-1034, Budapest, Hungary

    Imre J. Rudas  & János Fodor  & 

  2. Systems Research Institute, Polish Academy of Sciences, Ul.Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Lovassy, R., Kóczy, L.T., Gál, L. (2010). Optimization in Fuzzy Flip-Flop Neural Networks. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds) Computational Intelligence in Engineering. Studies in Computational Intelligence, vol 313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15220-7_27

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  • DOI: https://doi.org/10.1007/978-3-642-15220-7_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15219-1

  • Online ISBN: 978-3-642-15220-7

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