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

Bodyanskiy Yevgeniy (), Pliss Iryna (), Vynokurova Olena (), Peleshko Dmytro () and Rashkevych Yuriy ()
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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

Information Technology and Management Science, 2017, vol. 20, issue 1, 6-11

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.

Keywords: Artificial neural networks; computational intelligence; data compression; machine learning (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:itmasc:v:20:y:2017:i:1:p:6-11:n:1

DOI: 10.1515/itms-2017-0001

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