Please use this identifier to cite or link to this item: http://ea.donntu.edu.ua:8080/jspui/handle/123456789/15951
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dc.contributor.authorСкобцов, Ю. А.-
dc.contributor.authorТкаченко, А. В.-
dc.contributor.authorSkobtsov, Y.A.-
dc.contributor.authorTkachenko, A. V.-
dc.date.accessioned2012-11-13T07:52:02Z-
dc.date.available2012-11-13T07:52:02Z-
dc.date.issued2005-
dc.identifier.citationНаукові праці Донецького національного технічного університету. Серія: “Обчислювальна техніка та автоматизація”. Випуск 90 — Донецьк: ДонНТУ, 2005en_US
dc.identifier.urihttp://ea.donntu.edu.ua/handle/123456789/15951-
dc.descriptionProblem of program realization of artificial neural networks for real-time system is considered. Best methods for neural network training are overviewed. Resilient backpropagation algorithm is considered as one of the most efficient weight adaptation method. Application of conjugate graph for gradient generation is shown. Problem of weight initialization is considered and Ngueyn-Widrow method is applied to give good first approximation. Program library implementing overviewed methods is developed on С High speed of the library is shown in comparison with Mathlab.en_US
dc.publisherДонНТУen_US
dc.subjectMathlaben_US
dc.subjectneural networksen_US
dc.subjectreal-time systemsen_US
dc.subjectсистемы реального времениen_US
dc.subjectнейронные сетиen_US
dc.titleПРОГРАММНАЯ РЕАЛИЗАЦИЯ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ОБУЧЕНИЯ В СИСТЕМАХ РЕАЛЬНОГО ВРЕМЕНИen_US
dc.title.alternativeProgram realization of neural networks for learning in real-time systemsen_US
dc.typeArticleen_US
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