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Random Search Algorithm with Self-Learning for Neural Network Training

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Abstract

We discuss a random search algorithm with self-learning designed for solving a problem of training of feedforward neural networks and compare it with gradient algorithms for neural network training with regard to criteria of accuracy and computational complexity.

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Funding

The work was supported by RBRF grant no. 19-07-00614.

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Authors and Affiliations

  1. Lomonosov Moscow State University, 119991, Moscow, Russia

    V. A. Kostenko & L. E. Seleznev

Authors
  1. V. A. Kostenko
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  2. L. E. Seleznev
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Corresponding authors

Correspondence to V. A. Kostenko or L. E. Seleznev.

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Kostenko, V.A., Seleznev, L.E. Random Search Algorithm with Self-Learning for Neural Network Training. Opt. Mem. Neural Networks 30, 180–186 (2021). https://doi.org/10.3103/S1060992X2102003X

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  • DOI: https://doi.org/10.3103/S1060992X2102003X

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