<?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>2215-2504</journal-id>
<journal-title><![CDATA[Revista Forestal Mesoamericana Kurú]]></journal-title>
<abbrev-journal-title><![CDATA[Kurú]]></abbrev-journal-title>
<issn>2215-2504</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2215-25042021000200029</article-id>
<article-id pub-id-type="doi">10.18845/rfmk.v19i43.5806</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelos de Regresión Logística Multinomial Ordinal y Redes Neuronales Artificiales para la clasificación de madera aserrada]]></article-title>
<article-title xml:lang="en"><![CDATA[Multinomial and Ordinal Logistic Regression Models and Artificial Neural Networks for lumber grading]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guera]]></surname>
<given-names><![CDATA[Ouorou Ganni Mariel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva]]></surname>
<given-names><![CDATA[José Antônio Aleixo da]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ferreira]]></surname>
<given-names><![CDATA[Rinaldo Luiz Caraciolo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Álvarez Lazo]]></surname>
<given-names><![CDATA[Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Barrero Medel]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Garofalo Novo]]></surname>
<given-names><![CDATA[Madelén C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Filho]]></surname>
<given-names><![CDATA[Moacyr Cunha]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva]]></surname>
<given-names><![CDATA[José Wesley Lima]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade Federal Rural de Pernambuco (PPGBEA/UFRPE Programa de Pós-Graduação em Biometria e Estatística Aplicada ]]></institution>
<addr-line><![CDATA[Recife-PE ]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidade Federal Rural de Pernambuco (DCFL/UFRPE) Laboratório de Biometria e Manejo Florestal (LBMF) Departamento de Ciência Florestal]]></institution>
<addr-line><![CDATA[Recife-PE ]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Pinar del Río &#8220;Hermanos Saíz Montes de Oca&#8221; Departamento Forestal ]]></institution>
<addr-line><![CDATA[Pinar del Río ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Pinar del Río &#8220;Hermanos Saíz Montes de Oca&#8221; Departamento de Matemáticas ]]></institution>
<addr-line><![CDATA[Pinar del Río ]]></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>18</volume>
<numero>43</numero>
<fpage>29</fpage>
<lpage>40</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-25042021000200029&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-25042021000200029&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-25042021000200029&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La clasificación de la madera aserrada es una de las actividades más subjetivas de la fase final del proceso de aserrado de trozas en los aserríos. La presente investigación tuvo como objetivo proponer herramientas de auxilio en la clasificación de madera aserrada de coníferas. El estudio se realizó en el aserrío Combate de Tenerías de la Empresa Forestal Integral (EFI) Macurije, Pinar del Río, Cuba. Los datos utilizados proceden de 259 tablas de Pinus caribaea var. caribaea clasificadas siguiendo las exigencias (24 variables) y clases establecidas por el clasificador de madera aserrada de coníferas utilizado en Cuba. Se procedió al ajuste de un modelo de Regresión Logística Multinomial Ordinal (RLMO) y al entrenamiento de Redes Neuronales Artificiales (RNAs). Los parámetros del modelo RLMO fueron estimados usando el método de máxima verosimilitud optimizada con el algoritmo de Newton-Raphson. El entrenamiento de las RNAs se realizó con el algoritmo Broyden-Fletcher-Goldfarb-Shanno (BFGS). El problema de multicolinearidad, casi siempre presente en modelaciones con numerosas variables predictivas, fue abordado con el análisis factorial, utilizando los factores retenidos como entradas de los modelos. Con base en el porcentaje de aciertos en la clasificación, la RNA RBF 24-8-4 fue superior a las ecuaciones de regresión logística ordinal.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Lumber classification is one of the most subjective activities of the final phase of log sawing process in sawmills. The objective of this research was to propose tools that assist in conifers lumber grading. The research was carried out at the sawmill Combate de Tenerías of Macurije integrated forest company, Pinar del Río, Cuba. The data used comes from 259 lumber pieces of Pinus caribaea var. caribaea classified following the requirements (24 variables) and classes established by the conifers lumber grader used in Cuba. We proceeded to fit a Multinomial and Ordinal Logistic Regression (MOLR) model and train Artificial Neural Networks (ANNs). The parameters of the MOLR model were estimated using the maximum likelihood method optimized with the Newton Raphson algorithm. ANNs were trained with Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The multicollinearity problem, usually present in modeling with numerous predictive variables, was addressed with factor analysis, using the factors retained as inputs to the models. Based on the percentage of correct classification, the ANN RBF 24-8-4 was superior to the ordinal logistic regression equations.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Factorial Analysis]]></kwd>
<kwd lng="en"><![CDATA[Radial Basis Function]]></kwd>
<kwd lng="en"><![CDATA[Multilayer Perceptron]]></kwd>
<kwd lng="es"><![CDATA[Análisis Factorial]]></kwd>
<kwd lng="es"><![CDATA[Función de Base Radial]]></kwd>
<kwd lng="es"><![CDATA[Perceptrón Multicapa]]></kwd>
</kwd-group>
</article-meta>
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<year>1998</year>
<publisher-loc><![CDATA[Blacksburg, Virginia ]]></publisher-loc>
<publisher-name><![CDATA[Virginia Polytechnic Institute]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
