NEURAL NETWORKS MODULE

Authors

  • O. I. Chumachenko National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”
  • A. T. Kot National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.56.12937

Keywords:

Hybrid neural networks, Kohonnen neural network, perceptron, learning algorithm

Abstract

It is considered a basic approach for hybrid neuron network creation. As an example, the counter propagation neural network is analyzed. It is effectively used for image processing. Two modes of this neuron network functioning are considered. They are: accreditation and interpolation. Interpolation mode permits to reveal more complex features and can supply more precise results. Based on this analysis it is developed a new hybrid structure that includes Kohonnen neural network and perceptron. It is proposed a learning algorithm of this hybrid neuron network.

Author Biographies

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

Technical Cybernetic Department

Candidate of Science (Engineering). Assosiate Professor

A. T. Kot, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Post-graduate student

References

V. V. Borisov, V. V. Kruglov, and A. S. Fedulov, Fuzzy models and networks. 2 nd ed., The stereotype. 2012. (in Russian)

Khaykin Saymon. Neural networks: full course, 2nd edition. 2006. (in Russian)

A. P. Rotshteyn, Intelligent identification technologies: fuzzy sets, neural networks, genetic algorithms. Monograph. Vinnitsa: "Universum-Vіnnitsya," 1999, 295 p. (in Russian)

J.-S. R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference Systems, IEEE Trans. Systems, Man & Cybernetics 23 (1993).

Y. Bengio, A. Courville, and P. Vincent, Representation Learning: A Review and New Perspectives. Department of computer science and operations research, U. Montreal. 2014.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks. 2010.

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How to Cite

Chumachenko, O. I., & Kot, A. T. (2018). NEURAL NETWORKS MODULE. Electronics and Control Systems, 2(56), 64–68. https://doi.org/10.18372/1990-5548.56.12937

Issue

Vol. 2 No. 56 (2018)

Section

COMPUTER-AIDED DESIGN SYSTEMS

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