STRUCTURAL-PARAMETRIC SYNTHESIS OF THE FEEDFORWARD NEURAL NETWORKS WITH SIGMOID PIECEWISE-TYPE NEURONS

Authors

  • M. Z. Zgurovsky Rector of National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”
  • O. I. Chumachenko National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”
  • V. S. Gorbatiuk National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.58.13508

Keywords:

Feedforward neural networks, time series, sigmoid piecewise, training algorithm

Abstract

The method of structural and parametric synthesis of feedforward neural networks is considered, which includes Sigmoid Piecewise neurons, used in the process of a predictive model construction. The article describes the principles of a new developed Sigmoid Piecewise neuron constructing. It is proposed the method of structural and parametric synthesis with a single layer of  Sigmoid Piecewise neurons.The training algorithm of proposed neural networks is developed. The results of the effectiveness research of Sigmoid Piecewise neurons on real samples are presented.

Author Biographies

M. Z. Zgurovsky, Rector of National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Doctor of Engineering Science. Professor

Academician of the National Academy of Sciences of Ukraine

O. I. Chumachenko, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Candidate of Science (Engineering). Assosiate Professor

V. S. Gorbatiuk, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Post-graduate student

References

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Issue

Vol. 4 No. 58 (2018)

Section

THEORY AND METHODS OF SIGNAL PROCESSING

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