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Comparing the efficiency of serial and parallel algorithms for training artificial neural networks using computer clusters

  • An estimation of the number of multiplication and addition operations for training artififfcial neural networks by means of consecutive and parallel algorithms on a computer cluster is carried out. The evaluation of the efficiency of these algorithms is developed. The multilayer perceptron, the Volterra network and the cascade-correlation network are used as structures of artififfcial neural networks. Different methods of non-linear programming such as gradient and non-gradient methods are used for the calculation of the weight coefficients.

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Author:Oleg V. Kryuchin, Alexander A. Arzamastsev, Klaus G. Troitzsch
Series (Volume no.):Arbeitsberichte, FB Informatik (2011,13)
Document Type:Part of Periodical
Date of completion:2011/09/29
Date of publication:2011/09/29
Publishing institution:Universität Koblenz-Landau, Campus Koblenz, Universitätsbibliothek
Release Date:2011/09/29
Tag:artififfcial neural networks; computer clusters; estimation of algorithm efficiency; parallel algorithms
Number of pages:30 Seiten
Institutes:Fachbereich 4 / Fachbereich 4
Fachbereich 4 / Institut für Wirtschafts- und Verwaltungsinformatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoEs gilt das deutsche Urheberrecht: § 53 UrhG