<?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>2007-0934</journal-id>
<journal-title><![CDATA[Revista mexicana de ciencias agrícolas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Mex. Cienc. Agríc]]></abbrev-journal-title>
<issn>2007-0934</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-09342016000501029</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Red neuronal artificial backpropagation versus modelos empíricos para estimación de radiación global diaria en Sinaloa, México]]></article-title>
<article-title xml:lang="en"><![CDATA[Backpropagation artificial neural network versus empirical models for estimating daily global radiation in Sinaloa, Mexico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cervantes-Osornio]]></surname>
<given-names><![CDATA[Rocio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arteaga Ramírez]]></surname>
<given-names><![CDATA[Ramón]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez Peña]]></surname>
<given-names><![CDATA[Mario A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ojeda Bustamante]]></surname>
<given-names><![CDATA[Waldo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,INIFAP  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma Chapingo Departamento de Irrigación ]]></institution>
<addr-line><![CDATA[ Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Mexicano de Tecnología del Agua  ]]></institution>
<addr-line><![CDATA[Jiutepec Morelos]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2016</year>
</pub-date>
<volume>7</volume>
<numero>5</numero>
<fpage>1029</fpage>
<lpage>1042</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-09342016000501029&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-09342016000501029&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-09342016000501029&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Se compararon los resultados de los promedios de radiación global diaria estimados con el modelo de red neuronal artificial (RNA) bakpropagation contra los obtenidos por los modelos empíricos Hargreaves, Angström-Prescott y los calibrados de estos. Se utilizó un modelo de red neuronal artificial backpropagation con el algoritmo Levenberg Marquardt para el pronóstico de los promedios diarios de radiación global de cuatro estaciones ubicadas en el distrito de riego 075 Valle del Fuerte, Los Mochis Sinaloa, México. La base de datos representa promedios diarios con vectores de 1 484 datos para entrenamiento, validación y prueba y 229 para pronóstico. Entre las variables de entrada proporcionadas por el Distrito de riego, fueron: temperatura mínima y temperatura máxima, otras fueron calculadas como: duración real de la insolación, fotoperiodo y radiación solar extraterrestre. Se obtuvieron escenarios con una, dos y tres capas ocultas, con diversos números de neuronas en cada capa oculta. La RNA e6{27} con las entradas temperatura mínima, máxima, horas brillo sol dividida por el fotoperiodo y radiación solar extraterrestre, obtuvo el mejor ajuste, con un RMSE de 1.6871 y R2 de 0.89 para los 1484 datos y para los 229, lo obtuvo el modelo Angström-Prescott calibrado con un RMSE de 2.2812 y R2 de 0.89. Para los 1 484 datos promedios, el escenario 6{27} presenta la mejor estimación de la radiación global diaria (R  s ) y es mejor que los modelos empíricos, sin embargo para los 229 datos el modelo Angström-Prescott calibrado presenta una estimación de Rs mejor al e6{27} de la RNA.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The results were compared of average daily global radiation model estimated with artificial neural network (RNA) backpropagation against those obtained by empirical models Hargreaves, Angström-Prescott and these calibrated. A model of backpropagation artificial neural network was used with Levenberg Marquardt algorithm for forecasting average daily global radiation four stations located in the irrigation district 075 Valle del Fuerte, Los Mochis Sinaloa, Mexico. The database represents daily averages with 1 484 data vectors for training, validation and test and 229 for prognosis. Among the input variables provided by the irrigation district they were: minimum temperature and maximum temperature, others were calculated as actual duration of sunshine, photoperiod and extraterrestrial solar radiation. The scenarios with one, two and three hidden layers with different numbers of neurons in each hidden layer was obtained. The RNA e6{27} with entries minimum temperature, maximum, hours shine sun divided by photoperiod and extraterrestrial solar radiation, obtained the best fit with a RMSE of 1.6871 and R2 of 0.89 for 1 484 and for data for 229, the AngstromPrescott won the calibrated model with RMSE of 2.2812 and R2 of 0.89. For 1484 average data, the e6{27} scenario presents the best estimate of daily global radiation (Rs) and is better than the empirical models, however for 229 data the Angstrom-Prescott calibrated model provides an estimate of Rs better e6{27} of the RNA.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Angström-Prescott]]></kwd>
<kwd lng="es"><![CDATA[Hargreaves]]></kwd>
<kwd lng="es"><![CDATA[promedios]]></kwd>
<kwd lng="es"><![CDATA[radiación solar]]></kwd>
<kwd lng="es"><![CDATA[red neuronal artificial]]></kwd>
<kwd lng="en"><![CDATA[Angström-Prescott]]></kwd>
<kwd lng="en"><![CDATA[Hargreaves]]></kwd>
<kwd lng="en"><![CDATA[averages]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[solar radiation]]></kwd>
</kwd-group>
</article-meta>
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