<?xml version="1.0" encoding="utf-8"?>
<!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">17</article-id>
      <title-group>
        <article-title>Artificial intelligence method and models for data analysis and decision making</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Методы и модели искусственного интеллекта в анализе данных и принятии решений</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Akhatova</surname>
            <given-names>Chulpan</given-names>
          </name>
          <email>аkhatova_chulpan@mail.ru</email>
        </contrib>
      </contrib-group>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2011-10-10">
        <day>10</day>
        <month>10</month>
        <year>2011</year>
      </pub-date>
      <issue>5</issue>
      <issue-id pub-id-type="publisher-id">133</issue-id>
      <fpage>99</fpage>
      <lpage>106</lpage>
      <abstract xml:lang="en">
        <p>In this article the fuzzy neural network model of the data intellectual analysis and of the reception of decision making rules is described. The algorithm of its training is offered. The results of the given algorithm efficiency comparison with other decisions are resulted. On an example of a medical diagnostics task the efficiency of the offered approaches use for the expert systems knowledge bases formation in various fields of human activity is shown.</p>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>knowledge representation model</kwd>
        <kwd>fuzzy neural network</kwd>
        <kwd>knowledge base</kwd>
        <kwd>expert system</kwd>
        <kwd>decision making</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
