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A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES

SAMUEL AYELE AYTENFSU


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{
  "@context": "https://schema.org/", 
  "@id": "https://doi.org/10.20372/nadre/4195", 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY", 
      "name": "SAMUEL AYELE AYTENFSU"
    }
  ], 
  "datePublished": "2019-10-25", 
  "description": "<p>The main function of a greenhouse is climate adaptation by the side of the interior of the<br>\nclosed structure; it aims to produce a perfect condition for the development and growth of<br>\nspecific crops. It also ensures isolation and protection against harsh weather conditions<br>\nand possible animal intrusions.<br>\nThis research proposed an Elman recurrent neural network prediction model to control<br>\nthe interior of greenhouses by considering four input parameters that influence the<br>\ngreenhouse. The input parameter considered is wind velocity,  concentration, outside<br>\ntemperature and humidity. This paper also considered the data acquisition technique for<br>\nacquiring the parameter. The data used for developing the model also part of the research<br>\npaper and acquired in the place of Addis Ababa by setting experiments in property of<br>\nEthiopian environmental and forest research institute.<br>\nThe data acquisition technique is very economical and done using the Arduino platform.<br>\nThe data after collected is filtered and normalized using the Min-Max normalization<br>\ntechnique. Then scaled all the data to {0 1} and finally, the model is developed using<br>\nElman neural network. Several selection criteria for fixing the proper number of a hidden<br>\nneuron are analyzed; 53 different criteria is tested based on evaluating the performance<br>\nusing different statistical error measurement. The design of a neural network is<br>\nimplemented based on these selection criteria using a convergence theorem. After<br>\nimplementation, to verify the effectiveness the proposed model simulation was conducted<br>\nin real-time inside temperature and inside relative humidity greenhouse data. The result<br>\nobtained a minimal MSE of 0.0218, MAE of 0.1016, and RMSE of 0.1477.<br>\nThe proposed model is simple, economical especially for developing countries, efficient<br>\nfor predicting with minimal error. These papers also include the comparison of other<br>\nmodels with the proposed and summarized in the table<br>\n&nbsp;</p>", 
  "headline": "A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES", 
  "identifier": "https://doi.org/10.20372/nadre/4195", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "keywords": [
    "Greenhouse Interior Control, Elman Neural Network, Prediction Model, Convergence Theorem,"
  ], 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES", 
  "url": "https://nadre.ethernet.edu.et/record/4195"
}
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