Thesis Open Access

# A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES

SAMUEL AYELE AYTENFSU

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"@type": "ScholarlyArticle",
"creator": [
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"@type": "Person",
"affiliation": "ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY",
"name": "SAMUEL AYELE AYTENFSU"
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"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",
"inLanguage": {
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"alternateName": "eng",
"name": "English"
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"keywords": [
"Greenhouse Interior Control, Elman Neural Network, Prediction Model, Convergence Theorem,"
],
"name": "A NEURAL NETWORK PREDICTION MODEL TO CONTROL THE INTERIOR OF GREENHOUSES",
}
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