Basic concepts of dynamic recurrent neural networks development

dc.contributor.authorBoyko, N.
dc.contributor.authorPobereyko, P.
dc.description.abstractIn this work formulated relevance, set out an analytical review of existing approaches to the research recurrent neural networks (RNN) and defined precondition appearance a new direction in the field neuroinformatics – reservoir computing. Shows generalized classification neural network (NN) and briefly described main types dynamics and modes RNN. Described topology, structure and features of the model NN with different nonlinear functions and with possible areas of progress. Characterized and systematized well-known learning methods RNN and conducted their classification by categories. Determined the place RNN with unsteady dynamics of other classes RNN. Deals with the main parameters and terminology, which used to describe models RNN. Briefly described practical implementation recurrent neural networks in different areas natural sciences and humanities, and outlines and systematized main deficiencies and the advantages of using different RNN. The systematization of known recurrent neural networks and methods of their study is performed and on this basis the generalized classification of neural networks was proposed.uk_UA
dc.identifier.citationBoyko N. Basic concepts of dynamic recurrent neural networks development / N. Boyko, P. Pobereyko // Econtechmod : an international quarterly journal on economics in technology, new technologies and modelling processes. – Lublin ; Rzeszow, 2016. – Volum 5, number 2. – P. 63–68. – Bibliography: 20 titles.uk_UA
dc.publisherCommission of Motorization and Energetics in Agricultureuk_UA
dc.subjectrecurrent neural networkuk_UA
dc.subjectdynamic systemuk_UA
dc.subjectlearning algorithmsuk_UA
dc.subjectreservoir computinguk_UA
dc.subjectunsteady dynamicsuk_UA
dc.titleBasic concepts of dynamic recurrent neural networks developmentuk_UA


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