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Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

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Engineering for Sustainable Future (INTER-ACADEMIA 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 101))

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Abstract

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.

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Acknowledgments

This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Institute of Advanced Studies Koszeg, Koszeg, Hungary

    Sina Ardabili

  2. Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary

    Amir Mosavi & Annamária R. Várkonyi-Kóczy

  3. School of the Built Environment, Oxford Brookes University, Oxford, OX3 0BP, UK

    Amir Mosavi

  4. Department of Biosystem Engineering, University of Tabriz, Tabriz, Iran

    Asghar Mahmoudi

  5. Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

    Tarahom Mesri Gundoshmian

  6. Institute of Business Studies, Szent Istvan University, Godollo, 2100, Hungary

    Saeed Nosratabadi

  7. Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    Annamária R. Várkonyi-Kóczy

Authors
  1. Sina Ardabili
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  2. Amir Mosavi
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  3. Asghar Mahmoudi
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  4. Tarahom Mesri Gundoshmian
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  5. Saeed Nosratabadi
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  6. Annamária R. Várkonyi-Kóczy
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Correspondence to Amir Mosavi .

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Editors and Affiliations

  1. Obuda University, Budapest, Hungary

    Annamária R. Várkonyi-Kóczy

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Ardabili, S., Mosavi, A., Mahmoudi, A., Gundoshmian, T.M., Nosratabadi, S., Várkonyi-Kóczy, A.R. (2020). Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks. In: Várkonyi-Kóczy, A. (eds) Engineering for Sustainable Future. INTER-ACADEMIA 2019. Lecture Notes in Networks and Systems, vol 101. Springer, Cham. https://doi.org/10.1007/978-3-030-36841-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-36841-8_3

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