Сonvergence of Sequential Gradient Learning Algorithms in Neural Networks for Online Identification of Nonlinear Systems: a Special Case

The paper deals with the asymptotic properties of an online learning procedure for identifying non-linear systems via neural networks models of these systems. The probabilistic convergence condi-tions of this procedure are presented for the special case where a nonlinearity can exactly be ap-proxima...

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Datum:2015
Hauptverfasser: Zhiteckii, L.S., Nikolaienko, S.A.
Format: Artikel
Sprache:English
Veröffentlicht: Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України 2015
Schriftenreihe:Індуктивне моделювання складних систем
Online Zugang:http://dspace.nbuv.gov.ua/handle/123456789/125021
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Назва журналу:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Zitieren:Сonvergence of Sequential Gradient Learning Algorithms in Neural Networks for Online Identification of Nonlinear Systems: a Special Case / L.S. Zhiteckii, S.A. Nikolaienko // Індуктивне моделювання складних систем: Зб. наук. пр. — К.: МННЦ ІТС НАН та МОН України, 2015. — Вип. 7. — С. 46-58. — Бібліогр.: 27 назв. — англ

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
Beschreibung
Zusammenfassung:The paper deals with the asymptotic properties of an online learning procedure for identifying non-linear systems via neural networks models of these systems. The probabilistic convergence condi-tions of this procedure are presented for the special case where a nonlinearity can exactly be ap-proximated by a suitable neural network. Keywords: identification, nonlinear system, neural network, learning algorithm, stochastic environment, convergence.