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Neo-Fuzzy Encoder and Its Adaptive Learning for Big Data Processing

Author

Listed:
  • Bodyanskiy Yevgeniy
  • Pliss Iryna

    (Kharkiv National University of Radio Electronics, Harkiv, Ukraine)

  • Vynokurova Olena

    (Kharkiv National University of Radio Electronics, Kharkiv, Ukraine)

  • Peleshko Dmytro

    (IT Step University, Liov, Ukraine)

  • Rashkevych Yuriy

    (Ministry of Education and Science of Ukraine, Kiev, Ukraine)

Abstract

In the paper a two-layer encoder is proposed. The nodes of encoder under consideration are neo-fuzzy neurons, which are characterised by high speed of learning process and effective approximation properties. The proposed architecture of neo-fuzzy encoder has a two-layer bottle neck” structure and its learning algorithm is based on error backpropagation. The learning algorithm is characterised by a high rate of convergence because the output signals of encoder’s nodes (neo-fuzzy neurons) are linearly dependent on the tuning parameters. The proposed learning algorithm can tune both the synaptic weights and centres of membership functions. Thus, in the paper the hybrid neo-fuzzy system-encoder is proposed that has essential advantages over conventional neurocompressors.

Suggested Citation

  • Bodyanskiy Yevgeniy & Pliss Iryna & Vynokurova Olena & Peleshko Dmytro & Rashkevych Yuriy, 2017. "Neo-Fuzzy Encoder and Its Adaptive Learning for Big Data Processing," Information Technology and Management Science, Sciendo, vol. 20(1), pages 6-11, December.
    • Handle: RePEc:vrs:itmasc:v:20:y:2017:i:1:p:6-11:n:1
    DOI: 10.1515/itms-2017-0001
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