HYBRID NEURON NETWORKS BASED ON Q- , W- AND CLASSICAL NEURONS

Authors

  • O. I. Chumachenko National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”
  • S. T. Dychko National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”
  • А. R. Rizhiy National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.62.14385

Keywords:

Hybrid neural network, structural-parametric synthesis, optimization problem

Abstract

The problem of structural-parametric synthesis of hybrid neural network based on the use of multilayer perceptron topology is considered. Hybridization is achieved through the use of artificial neurons of different types, namely Q-neuron, W-neuron and classical neuron. The problem of optimal selection of the number of layers, neurons in layers, as well as the types of neurons in each layer and the principles of alternating them using the genetic algorithm SPEA2 is solved. Examples of building a hybrid neural network using this methodology and a given optimization criterion for solving classification and forecasting problems are given.

Author Biographies

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

Technical Cybernetic Departament

Candidate of Science. (Engineering). Associate Professor

orcid.org/0000-0003-3006-7460

S. T. Dychko, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Undergraduate student

А. R. Rizhiy, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Undergraduate student

References

Ye. Bodyanskiy and O. Vynokurova, “Compartmental adaptive wavelon and its learning algorithm,” Control systems and computers 1, pp. 47–53, 2009.

Ye. Bodyanskiy, O. Vynokurova, A. Dolotov and O. Kharchenko, “Wavelet-Neuro-Fuzzy-Network Structure Optimization Using GMDH for the Solving Forecasting Tasks,” International Conference in Inductive Modelling, 2013.

Ye. Bodyanskiy, I. Pliss, and O. Vynokurova, "Hybrid GMDH-neural network of computational intelligence," Proc. 3rd International Workshop on Inductive Modelling, Poland, Krynica, 2009, рр. 100–107.

Ye. Bodyanskiy, O. Vynokurova, and N. Teslenko, "Cascade GMDH-Wavelet-Neuro-Fuzzy Network," The 4th International Workshop on Inductive ModellingIWIM 2011, рр. 22–30.

Е. А. Vinokurova, " Generalized multidimensional wavelet-neuro-fuzzy system in computational intelligence problems," Intelligent decision-making systems and problems of computational intelligence: a collection of scientific papers based on the materials of an international scientific conference. Evpatoriay–Cherson, vol. 2, 2010, pp. 329–333.

E. Zitzler, M. Laumanns and L. Thiele, “SPEA2: Improving the Strength Pareto approach,” Swiss Federal Institute Technology, 2001.

Optical Recognition of Handwritten Digits Data Set [Online]. Avaible: https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

Daily Minimum Temperatures In Melbourne [Online]. Avaible: https://www.kaggle.com/paulbrabban/daily-minimum-temperatures-in-melbourne

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Issue

Vol. 4 No. 62 (2019)

Section

COMPUTER-AIDED DESIGN SYSTEMS

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