STRUCTURAL-PARAMETRIC SYNTHESIS OF HYBRID NEURAL NETWORKS ENSEMBLES

Authors

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

DOI:

https://doi.org/10.18372/1990-5548.54.12323

Keywords:

Neural networks, ensemble, training, optimization, topology

Abstract

It is considered the approach to the design of the ensemble of neural networks, where a collection of a finite number of neural networks is trained for the same task, then their results of the given task solution are combined. It is proposed an algorithm of optimal choice of neural networks topologies and their quantity for their inclusion as a member in ensemble. The further refinement of ensemble composition is done with help pruning operation. The output of an ensemble is a weighted average of the outputs of each network, with the ensemble weights determined as a function of the relative error of each network determined in training. It is presented a novel approach to determine the ensemble weights dynamically as part of the training algorithm. The weights are proportional to the certainty of the respective outputs.

Author Biographies

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

Technical Cybernetic Department

Candidate of Science (Engineering). Assosiate Professor

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

Technical Cybernetic Department

Post-graduate student

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Issue

Vol. 4 No. 54 (2017)

Section

COMPUTER-AIDED DESIGN SYSTEMS

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