Thesis Open Access

A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES

SAMUEL AYELE AYTENFSU


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  <identifier identifierType="DOI">10.20372/nadre/4195</identifier>
  <creators>
    <creator>
      <creatorName>SAMUEL AYELE AYTENFSU</creatorName>
      <affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES</title>
  </titles>
  <publisher>National Academic Digital Repository of Ethiopia</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Greenhouse Interior Control, Elman Neural Network, Prediction Model, Convergence Theorem,</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Supervisor">
      <contributorName>Asrat Mulatu (PhD)</contributorName>
      <affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
    </contributor>
    <contributor contributorType="Supervisor">
      <contributorName>Mr. Tameru H/Sellasie</contributorName>
      <affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2019-10-25</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Thesis</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/4195</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.20372/nadre/4194</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/aastu</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/nadre</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The main function of a greenhouse is climate adaptation by the side of the interior of the&lt;br&gt;
closed structure; it aims to produce a perfect condition for the development and growth of&lt;br&gt;
specific crops. It also ensures isolation and protection against harsh weather conditions&lt;br&gt;
and possible animal intrusions.&lt;br&gt;
This research proposed an Elman recurrent neural network prediction model to control&lt;br&gt;
the interior of greenhouses by considering four input parameters that influence the&lt;br&gt;
greenhouse. The input parameter considered is wind velocity,  concentration, outside&lt;br&gt;
temperature and humidity. This paper also considered the data acquisition technique for&lt;br&gt;
acquiring the parameter. The data used for developing the model also part of the research&lt;br&gt;
paper and acquired in the place of Addis Ababa by setting experiments in property of&lt;br&gt;
Ethiopian environmental and forest research institute.&lt;br&gt;
The data acquisition technique is very economical and done using the Arduino platform.&lt;br&gt;
The data after collected is filtered and normalized using the Min-Max normalization&lt;br&gt;
technique. Then scaled all the data to {0 1} and finally, the model is developed using&lt;br&gt;
Elman neural network. Several selection criteria for fixing the proper number of a hidden&lt;br&gt;
neuron are analyzed; 53 different criteria is tested based on evaluating the performance&lt;br&gt;
using different statistical error measurement. The design of a neural network is&lt;br&gt;
implemented based on these selection criteria using a convergence theorem. After&lt;br&gt;
implementation, to verify the effectiveness the proposed model simulation was conducted&lt;br&gt;
in real-time inside temperature and inside relative humidity greenhouse data. The result&lt;br&gt;
obtained a minimal MSE of 0.0218, MAE of 0.1016, and RMSE of 0.1477.&lt;br&gt;
The proposed model is simple, economical especially for developing countries, efficient&lt;br&gt;
for predicting with minimal error. These papers also include the comparison of other&lt;br&gt;
models with the proposed and summarized in the table&lt;br&gt;
&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
</resource>
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