<?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>0123-921X</journal-id>
<journal-title><![CDATA[Tecnura]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnura]]></abbrev-journal-title>
<issn>0123-921X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-921X2023000100072</article-id>
<article-id pub-id-type="doi">10.14483/22487638.18623</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Redes neuronales aplicadas al control estadístico de procesos con cartas de control EWMA]]></article-title>
<article-title xml:lang="en"><![CDATA[Neural networks applied to statistical process control with EWMA control charts]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Suárez Castro]]></surname>
<given-names><![CDATA[Ruth Milena]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ladino Vega]]></surname>
<given-names><![CDATA[Iván Darío]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Fundación Universitaria Los Libertadores  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Fundación Universitaria Los Libertadores  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<volume>27</volume>
<numero>75</numero>
<fpage>72</fpage>
<lpage>88</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-921X2023000100072&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-921X2023000100072&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-921X2023000100072&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto: Existe una creciente necesidad de monitorear y predecir variables críticas en procesos productivos; por tanto, a partir del enfoque de control estadístico, se ha asumido desde el uso de cartas de control para mediciones individuales. Así, en este artículo se presentan los resultados del diseño de una red neuronal recurrente long short term memory (LSTM) para predecir el valor promedio de la variable temperatura en mediciones individuales, y así evaluar la capacidad de la red para obtener valores similares a los cálculos del promedio móvil ponderado EWMA para mediciones individuales.  Método: Se obtuvieron 1768 registros de mediciones individuales de temperatura realizadas por un sensor, en el conjunto de datos denominado gas sensors for home activity monitoring data set. Los datos de temperatura se representaron en una carta de control de promedios móviles ponderados exponenciales EWMA, con el fin de obtener los valores de la media del proceso y de identificar que este estuviera dentro del control estadístico. Posteriormente, se entrenó una red neuronal LSTM a una muestra de entrenamiento de 1184 datos con algoritmo backpropagation que permitiera obtener valores similares a EWMA, los cuales se validaron en una muestra de prueba de 584 datos de temperatura.  Resultados: Se obtuvo el diseño de una red neuronal con una unidad en la puerta de entrada, cuatro en la puerta de olvido y una en la puerta de salida entrenada con el algoritmo Backpropagation, la cual permitió calcular valores muy cercanos a los representados en la carta de control EWMA, con un MSE de 1.1405e-04.  Conclusiones: Las redes neuronales LSTM son una buena alternativa para el cálculo de valores EWMA, cuando se requiera hacer control estadístico de un proceso que genera gran cantidad de datos obtenidos de mediciones y no se cuente con un software para procesarlos.  Financiación:  Fundación Universitaria Los Libertadores]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Context:  There is a growing need to monitor and predict critical variables in production processes, from the statistical process control approach it has been assumed from the use of control charts for individual measurements, for that reason this article presents the results of the design of a long short term memory (LSTM) recurrent neural network to predict the average value of the variable temperature in individual measurements and thus evaluate the ability of the network to obtain values similar to the EWMA weighted moving average calculations for individual measurements. Being this  Methodology: 1768 records of individual temperature measurements made by a sensor were obtained, in the data set called: Gas sensors for home activity monitoring data set. Temperature data was plotted on an EWMA exponential weighted moving average control chart to obtain process mean values and identify that the process was within statistical control. Subsequently, an LSTM neural network was trained on a training sample of 1184 data with a Backpropagation algorithm that allowed obtaining values similar to EWMA, which were validated in a test sample of 584 temperature data.  Results: The design of a neural network with a unit in the input gate, 4 units in the forgetting gate and 1 unit in the output gate was obtained, trained with the Backpropagation algorithm, it allowed to calculate values very close to those represented in the control chart. EWMA, with an MSE of 1.1405e-04.  Conclusions: LSTM neural networks are a good alternative for calculating EWMA values, when statistical control of a process that generates a large amount of data obtained from measurements is required and there is no software to process them.  Financing: Fundación universitaria Los Libertadores]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[redes neuronales LSTM]]></kwd>
<kwd lng="es"><![CDATA[carta control]]></kwd>
<kwd lng="es"><![CDATA[EWMA]]></kwd>
<kwd lng="es"><![CDATA[temperatura]]></kwd>
<kwd lng="en"><![CDATA[LSTM neural networks]]></kwd>
<kwd lng="en"><![CDATA[control chart]]></kwd>
<kwd lng="en"><![CDATA[EWMA]]></kwd>
<kwd lng="en"><![CDATA[temperature]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Addeha]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Khormalib]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Golilarz]]></surname>
<given-names><![CDATA[N. A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Control chart pattern recognition using RBF neural network with new training algorithm and practical features]]></article-title>
<source><![CDATA[ISA Transactions]]></source>
<year>2018</year>
<numero>79</numero>
<issue>79</issue>
<page-range>202-12</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Aparisi]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Carrión-García]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<source><![CDATA[Artificial neural networks for identifying the signals of multivariate EWMA control charts]]></source>
<year>2010</year>
<conf-name><![CDATA[ 10International Conference on Intelligent Systems Design and Applications]]></conf-name>
<conf-loc> </conf-loc>
<page-range>427-31</page-range><publisher-name><![CDATA[ISDA]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Awadallaa]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Abdellatif Sadekb]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Spiking neural network-based control chart pattern]]></article-title>
<source><![CDATA[Alexandria Engineering Journal]]></source>
<year>2012</year>
<volume>51</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>27-35</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Berzal]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<source><![CDATA[Redes neuronales y deep learning]]></source>
<year>2018</year>
<publisher-name><![CDATA[Edición independiente]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Beshah]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Muluneh]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Control chart pattern recognition of multivariate auto-correlated processes using artificial neural network]]></article-title>
<source><![CDATA[Journal of EEA]]></source>
<year>2017</year>
<numero>35</numero>
<issue>35</issue>
<page-range>47-57</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[C.-S.]]></given-names>
</name>
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[S.-S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A neural network-based procedure for the monitoring of exponential mean]]></article-title>
<source><![CDATA[Computers &amp; Industrial Engineering]]></source>
<year>2001</year>
<numero>40</numero>
<issue>40</issue>
<page-range>309-21</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[C.-S.]]></given-names>
</name>
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[H.-P]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Using neural networks to detect the bivariate process variance shifts pattern]]></article-title>
<source><![CDATA[Computers &amp; Industrial Engineering]]></source>
<year>2011</year>
<volume>60</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>269-78</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[K.-Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Shaw]]></surname>
<given-names><![CDATA[Y.-C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Applying back propagation network to cold chain temperature monitoring]]></article-title>
<source><![CDATA[Advanced Engineering Informatics]]></source>
<year>2010</year>
<volume>25</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>11-22</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[C.-S.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[P.-W.]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[K.-K]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimating the shift size in the process mean with support vector regression]]></article-title>
<source><![CDATA[Expert Systems with Applications]]></source>
<year>2011</year>
<numero>38</numero>
<issue>38</issue>
<page-range>10624-30</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chiñas-Sánchez]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Vázquez-López]]></surname>
<given-names><![CDATA[J. A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multivariate variables recognition using Hotelling's T2 and MEWMA via ANN's]]></article-title>
<source><![CDATA[Ingeniería, Investigación y Tecnología]]></source>
<year>2014</year>
<volume>15</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>125-38</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Elsheikh]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Katekar]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Muskens]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Deshmukh]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Elaziz]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Dabour]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate.]]></article-title>
<source><![CDATA[Process Safety and Environmental Protection]]></source>
<year>2021</year>
<volume>148</volume>
<page-range>273-82</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Flores Sánchez]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<source><![CDATA[Nuevas aportaciones del análisis de datos funcionales en el control estadístico de procesos]]></source>
<year>2018</year>
<publisher-name><![CDATA[Universidad de Coruña]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Flórez López]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Fernández]]></surname>
<given-names><![CDATA[J. M]]></given-names>
</name>
</person-group>
<source><![CDATA[Las redes neuronales artificiales. Fundamentos teóricos y aplicaciones prácticas]]></source>
<year>2008</year>
<publisher-name><![CDATA[Netbiblo]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fuquaa]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Razzaghi]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A cost-sensitive convolution neural network learning for control chart pattern recognition]]></article-title>
<source><![CDATA[Expert Systems with Applications]]></source>
<year>2020</year>
<numero>150</numero>
<issue>150</issue>
<page-range>113-275</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Govindarajana]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Llueguerab]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Melero]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Molero]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Soler]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[El control estadístico de proceso puede ayudar a prevenir los errores de tratamiento sin aumentar los costes en radioterapia]]></article-title>
<source><![CDATA[Revista de Calidad Asistencial]]></source>
<year>2010</year>
<volume>25</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>281-90</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Herrera Acosta]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Romero Cabrera]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Wasinki-Zúñiga]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Contraste entre las cartas de control MR Shewart y Cusum varianza en el monitoreo del potencial de hidrógeno en protectores de planta]]></article-title>
<source><![CDATA[Iteckne, Innovación e Investigación en Ingeniería]]></source>
<year>2018</year>
<volume>15</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>88-98</page-range></nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Maisueche Cuadrado]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<source><![CDATA[Utilización del machine learning en la industria 4.0]]></source>
<year>2019</year>
<publisher-name><![CDATA[Repositorio institucional de la Universidad de Valladolid]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Montgomery]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<source><![CDATA[Control estadístico de la calidad]]></source>
<year>2013</year>
<publisher-name><![CDATA[Limusa Willey]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Montiel Ariza]]></surname>
<given-names><![CDATA[H. M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Using neural networks for face recognition in controlled environments]]></article-title>
<source><![CDATA[Tecnura]]></source>
<year>2015</year>
<numero>19</numero>
<issue>19</issue>
<page-range>67-77</page-range></nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Núñez Castro]]></surname>
<given-names><![CDATA[J. F]]></given-names>
</name>
</person-group>
<source><![CDATA[Aprendizaje automático en fusión nuclear con deep learning]]></source>
<year>2017</year>
<publisher-name><![CDATA[Pontificia Universidad Católica de Valparaíso]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Olah]]></surname>
<given-names><![CDATA[Ch]]></given-names>
</name>
</person-group>
<source><![CDATA[Recurrent neural networks]]></source>
<year></year>
<publisher-name><![CDATA[Colah&#8217;s Blog]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Peláez Chávez]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<source><![CDATA[Aprendizaje no supervisado y el algoritmo wake sleep en redes neuronales]]></source>
<year>2012</year>
<publisher-name><![CDATA[Universidad Tecnológica de la Mixteca]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pérez Verona]]></surname>
<given-names><![CDATA[I. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Arco García]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Una revisión sobre aprendizaje no supervisado de métricas de distancia]]></article-title>
<source><![CDATA[Revista Cubana de Ciencias Informáticas]]></source>
<year>2016</year>
<volume>10</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>43-67</page-range></nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Quintana]]></surname>
<given-names><![CDATA[A. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Pisani]]></surname>
<given-names><![CDATA[M. V.]]></given-names>
</name>
<name>
<surname><![CDATA[Casal]]></surname>
<given-names><![CDATA[R. N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Desempeño de cartas de control estadístico con límites bilaterales de probabilidad para monitorear procesos Weibull en mantenimiento]]></article-title>
<source><![CDATA[Ingeniería, Investigación y Tecnología]]></source>
<year>2015</year>
<volume>16</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>143-56</page-range></nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rios]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Alanis]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Arana-Daniel]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[López-Franco]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Appendix A - Artificial neural networks]]></article-title>
<source><![CDATA[Neural networks modeling and control. Applications for unkown nonlinear delayed systems in discrete time]]></source>
<year>2020</year>
<page-range>117-24</page-range><publisher-name><![CDATA[Academic Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rius]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ruisanchez]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Callao]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Rius]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Reliability of analytical systems: Use of control charts, time series models and recurrent neural networks RNN]]></article-title>
<source><![CDATA[Chemometrics and Intelligent Laboratory Systems]]></source>
<year>1998</year>
<numero>40</numero>
<issue>40</issue>
<page-range>1-18</page-range></nlm-citation>
</ref>
<ref id="B27">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rivas Asanza]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Mazón Olivo]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<source><![CDATA[Redes neuronales artificiales aplicadas al reconocimiento de patrones]]></source>
<year>2018</year>
<publisher-name><![CDATA[Editorial UTMACH]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B28">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Santolamazza]]></surname>
<given-names><![CDATA[A. C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Anomaly detection in energy consumption for condition-based maintenance of compressed air generation systems: An approach based on artificial neural networks]]></article-title>
<source><![CDATA[IFAC-PapersOnLine]]></source>
<year>2018</year>
<volume>51</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>1131-6</page-range></nlm-citation>
</ref>
<ref id="B29">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Serrano]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Soria]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Martín]]></surname>
<given-names><![CDATA[J. D]]></given-names>
</name>
</person-group>
<source><![CDATA[Redes neuronales artificiales]]></source>
<year>2010</year>
<publisher-name><![CDATA[Universidad de Valencia]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B30">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Theodorids]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Chapter 18 - Neural networks and deep learning]]></article-title>
<source><![CDATA[Machine learning: A Bayesian and optimization perspective]]></source>
<year>2020</year>
<page-range>901-1038</page-range><publisher-name><![CDATA[Academic Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B31">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Torres Álvarez]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Hernández]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Pedraza]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Redes neuronales y predicción de tráfico]]></article-title>
<source><![CDATA[Tecnura]]></source>
<year>2011</year>
<numero>15</numero>
<issue>15</issue>
<page-range>90-7</page-range></nlm-citation>
</ref>
<ref id="B32">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Truong Pham]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Packianather]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Afify]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial neural networks]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Andina]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Pham]]></surname>
<given-names><![CDATA[D. Truong]]></given-names>
</name>
</person-group>
<source><![CDATA[Computacional intelligence: For engineering and manufacturing]]></source>
<year>2007</year>
<page-range>67-92</page-range></nlm-citation>
</ref>
<ref id="B33">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vergara Benavides]]></surname>
<given-names><![CDATA[M. C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Aplicación de la carta de control EWMA-CV para la optimización del monitoreo del peso en la leche pasteurizada en bolsa]]></article-title>
<source><![CDATA[Revista Científica Tecknos]]></source>
<year>2012</year>
<volume>8</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>7-16</page-range></nlm-citation>
</ref>
<ref id="B34">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vieira]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[López Pinaya]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Garcia-Dias]]></surname>
<given-names><![CDATA[R. M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Chapter 9 - Deep neural networks]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Vieira]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Mechelli]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<source><![CDATA[Machine learning methods and applications to brain disorders]]></source>
<year>2020</year>
<page-range>157-72</page-range><publisher-name><![CDATA[Academic Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B35">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Villarreal]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Arango]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estrategias para el entrenamiento de redes neuronales de números difusos]]></article-title>
<source><![CDATA[Tecnura]]></source>
<year>2013</year>
<numero>18</numero>
<issue>18</issue>
<page-range>36-47</page-range></nlm-citation>
</ref>
<ref id="B36">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Ai]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran]]></article-title>
<source><![CDATA[Chaos, Solitons &amp; Fractals]]></source>
<year>2020</year>
<volume>140</volume>
<numero>110240</numero>
<issue>110240</issue>
</nlm-citation>
</ref>
<ref id="B37">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Lv]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhuan]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Lu]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Zou]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Qin]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Control chart pattern recognition method based on improved one-dimensional convolutional neural network]]></article-title>
<source><![CDATA[IFAC-PapersOnLine]]></source>
<year>2019</year>
<volume>52</volume>
<numero>13</numero>
<issue>13</issue>
<page-range>1537-42</page-range></nlm-citation>
</ref>
<ref id="B38">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[X.-S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[8-Neural networks and deep learning]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[X.-S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Introduction to algorithms for data mining and machine learning]]></source>
<year>2019</year>
<page-range>139-61</page-range><publisher-name><![CDATA[Academic Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B39">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Jin]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Data-driven anomaly detection approach for time series streaming data]]></article-title>
<source><![CDATA[Sensors]]></source>
<year>2020</year>
<volume>20</volume>
<numero>19</numero>
<issue>19</issue>
<page-range>5646</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
