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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "https://jats.nlm.nih.gov/publishing/1.3/JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xml:lang="ru">
  <front xmlns:xlink="http://www.w3.org/1999/xlink">
    <journal-meta>
      <journal-title-group>
        <journal-title>Computing, Telecommunication and Control</journal-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Информатика, телекоммуникации и управление</trans-title>
        </trans-title-group>
      </journal-title-group>
      <issn pub-type="epub">2687-0517</issn>
    </journal-meta>
    <article-meta xmlns:xlink="http://www.w3.org/1999/xlink">
      <article-id pub-id-type="publisher-id">3</article-id>
      <title-group>
        <article-title>Recurrent neural network as dynamical system and approaches to its training</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Рекуррентная нейронная сеть как динамическая система и подходы к ее обучению</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Benderskaya</surname>
            <given-names>Elena</given-names>
          </name>
          <xref ref-type="aff" rid="aff1"/>
          <email>helen.bend@gmail.com</email>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Nikitin</surname>
            <given-names>Kirill</given-names>
          </name>
          <xref ref-type="aff" rid="aff2"/>
          <email>execiter@mail.ru</email>
        </contrib>
      </contrib-group>
      <aff id="aff1">Peter the Great St. Petersburg Polytechnic University</aff>
      <aff id="aff2">Peter the Great St.Petersburg Polytechnic University</aff>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2013-08-31">
        <day>31</day>
        <month>08</month>
        <year>2013</year>
      </pub-date>
      <issue>4</issue>
      <issue-id pub-id-type="publisher-id">176</issue-id>
      <fpage>29</fpage>
      <lpage>40</lpage>
      <self-uri xmlns:xlink="http://www.w3.org/1999/xlink" content-type="pdf" xlink:href="https://infocom.spbstu.ru/userfiles/files/articles/2013/4/03.pdf"/>
      <abstract xml:lang="en">
        <p>This paper presents the results of analytical study of recurrent neural networks (RNN) and their summary classification, done from the position of dynamical systems with regard to a new type of RNN – reservoir RNN. Knowledge systematization in the subject field allowed to distinguish main dynamical regimes of RNN and to describe the most perspective trends in the development of training algorithms in terms of the detected advantages and drawbacks of existing approaches.</p>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>recurrent neural network</kwd>
        <kwd>dynamical system</kwd>
        <kwd>training algorithms</kwd>
        <kwd>reservoir computing</kwd>
        <kwd>unstable dynamics</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
