Deep learning fuzzy classifier

Authors

  • V. M. Sineglazov National Aviation University, Kyiv
  • R. S. Koniushenko National Aviation University, Kyiv

DOI:

https://doi.org/10.18372/1990-5548.60.13813

Keywords:

Neural network, fuzzy neural network, deep learning

Abstract

It is considered a classification problem solution based on analysys of represented review. It’s shown that the neural networks have important advantages beside other methods, such as: classification using the nearest neighbor method, support vector classification, classification using decision trees, etc. Amount of artificial neural networks exists futher networks have the simplest structure, but the precision of the solution can be increased with help of deep learning approach, which is supposes the use of additional neural network for the solution of pretraining tasks(deep believe networks). It’s proposed new tophology which consist of: Takagi-Sugeno-Kang fuzzy classifier and Limited Boltzmann Machine neural network. Despite on this thopology was proposed early in this article it’s carried out enough researches that permited to specify the learning algorithm. An example of proposed algorithm implantation is represented.

Author Biographies

V. M. Sineglazov, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department, Faculty of Air Navigation Electronics and Telecommunications

Doctor of Engineering Science. Professor. Head of the Department

ORCID 0000-0002-3297-9060

R. S. Koniushenko, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department, Faculty of Air Navigation Electronics and Telecommunications

Bachalour

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Issue

Vol. 2 No. 60 (2019)

Section

COMPUTER-AIDED DESIGN SYSTEMS

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