<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2218-3620</journal-id>
<journal-title><![CDATA[Revista Universidad y Sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[Universidad y Sociedad]]></abbrev-journal-title>
<issn>2218-3620</issn>
<publisher>
<publisher-name><![CDATA[Editorial "Universo Sur"]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2218-36202021000600514</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Redes neuronales artificiales para estimar propiedades físicas, termodinámicas y de equilibrio de mezclas etanol-agua]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial neural network to estimate physical, thermodynamic and equilibrium properties of ethanol-water mixtures]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gutiérrez Labrador]]></surname>
<given-names><![CDATA[Julio César]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Ones]]></surname>
<given-names><![CDATA[Osney]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zumalacárregui de Cárdenas]]></surname>
<given-names><![CDATA[Lourdes]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de La Habana &#8220;José Antonio Echevarría&#8221;  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>13</volume>
<numero>6</numero>
<fpage>514</fpage>
<lpage>525</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2218-36202021000600514&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2218-36202021000600514&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2218-36202021000600514&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El presente trabajo tiene como objetivo desarrollar redes neuronales para la predicción de propiedades físicas, termodinámicas y de equilibrio de la mezcla etanol-agua, a partir de la recopilación de datos existentes en la literatura. Se realiza la limpieza de la base de datos y su transformación aplicando el criterio de los máximos. Se utiliza como tipo de red la perceptrón multicapas con tres capas y para el entrenamiento se usa la retroalimentación hacia atrás, con funciones de activación sigmoidea e hiperbólica. El error cuadrático medio (ECM), el coeficiente de regresión (R), así como el error relativo promedio (ERP) y la prueba de Friedman, son los criterios empleados para determinar la mejor red. La importancia relativa de las variables independientes en la predicción se determina mediante el empleo del método de los pesos. Se diseña una interfaz de usuario personalizada, que unifica las redes neuronales seleccionadas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The purpose of this work is to development neural networks for the prediction of physical, thermodynamic and equilibrium properties of the ethanol-water mixture, based on the compilation of existing data in the literature consulted. The database is cleaned and processed using the criterion of the maximum heat of each variable. The multi-layered perceptron with three layers and the backward feedback for the training are used as a network type, with functions of sigmoidal and hyperbolic activation. The mean squared error (MSE), the regression coefficient (R), the mean relative error (MRE) and the Friedman test, are the criteria used to determine the most appropriate network. The relative importance of the independent variables in the prediction is determined by the use of the weights&#8217; method. A customized user interface is designed, which unifies the selected neural networks.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[propiedades físicas]]></kwd>
<kwd lng="es"><![CDATA[propiedades termodinámicas]]></kwd>
<kwd lng="es"><![CDATA[propiedades de equilibrio]]></kwd>
<kwd lng="es"><![CDATA[interfaz de usuario]]></kwd>
<kwd lng="en"><![CDATA[Artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[physical properties]]></kwd>
<kwd lng="en"><![CDATA[thermodynamics properties]]></kwd>
<kwd lng="en"><![CDATA[equilibrium properties]]></kwd>
<kwd lng="en"><![CDATA[user interface]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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