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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="en">
  <front>
    <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>
      <article-id pub-id-type="publisher-id">15</article-id>
      <title-group>
        <article-title>The missing value estimation in numerical modelling of complex dynamic systems</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Восстановление пропущенных данных при численном моделировании сложных динамических систем</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Karlov</surname>
            <given-names>Ivan</given-names>
          </name>
          <email>IAKarlov@sfu-kras.ru</email>
        </contrib>
      </contrib-group>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2013-12-10">
        <day>10</day>
        <month>12</month>
        <year>2013</year>
      </pub-date>
      <issue>6</issue>
      <issue-id pub-id-type="publisher-id">186</issue-id>
      <fpage>137</fpage>
      <lpage>144</lpage>
      <abstract xml:lang="en">
        <p>This paper is deals with the problem of missing values in the modelling of complex dynamic systems. Considered different types of missing values and general approaches to dealing with missing data. This paper provides an overview of the most common methods of missing data estimation, and presents an original hybrid adaptive method of estimation with neuro-fuzzy control. Evaluate the effectiveness of various methods of missing data estimating applied to data sets that contain information about the processes in complex dynamic systems. Special attention is paid to the impact of missing data presence on the effectiveness of the models.</p>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>missing data</kwd>
        <kwd>neural networks</kwd>
        <kwd>fuzzy logic</kwd>
        <kwd>numerical modelling</kwd>
        <kwd>hybrid systems</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
