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Sibirskii Zhurnal Vychislitel'noi Matematiki, 2013, Volume 16, Number 1, Pages 1–9 (Mi sjvm493)  
Theorem of training for a competition algorithm

V. S. Antyufeev

Institute of Computational Mathematics and Mathematical Geophysics (Computing Center), Siberian Branch of the Russian Academy of Sciences, Novosibirsk
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Abstract: This paper is an extension of [1], where a new decision algorithm was proposed. In its operation, the unit resembles artificial neural networks. However the functioning of the algorithm proposed is based on the different concepts. It does not use the concept of a net, a neuron. The theorem of training for the new competition algorithm is proved.
Key words: theorem of training, probabilistic convergence, artificial neural network.
Received: 17.11.2011
Revised: 16.12.2011
English version:
Numerical Analysis and Applications, 2013, Volume 6, Issue 1, Pages 1–8
DOI: https://doi.org/10.1134/S1995423913010011
Bibliographic databases:
Document Type: Article
UDC: 519.633
Language: Russian
Citation: V. S. Antyufeev, “Theorem of training for a competition algorithm”, Sib. Zh. Vychisl. Mat., 16:1 (2013), 1–9; Num. Anal. Appl., 6:1 (2013), 1–8
Citation in format AMSBIB
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\jour Num. Anal. Appl.
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