Ardabili, S.; Mosavi, A.; Mahmoudi, A.; Gundoshmian, T.M.; Nosratabadi, S.; Varkonyi-Koczy, A.R. Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks. Preprints2019, 2019080201. https://doi.org/10.20944/preprints201908.0201.v1
APA Style
Ardabili, S., Mosavi, A., Mahmoudi, A., Gundoshmian, T.M., Nosratabadi, S., & Varkonyi-Koczy, A.R. (2019). Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints201908.0201.v1
Chicago/Turabian Style
Ardabili, S., Saeed Nosratabadi and Annamaria R. Varkonyi-Koczy. 2019 "Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks" Preprints. https://doi.org/10.20944/preprints201908.0201.v1
Abstract
Recent advancements of computer and electronic systems have motivated the extensive use of intelligent systems for automation of agricultural industries. In this study, the temperature variation of the mushroom growing room is modeled through using a multi-layered perceptron (MLP) and radial basis function networks. Modeling has been done based on the 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 is found to be the second repetition with 12 neurons in the hidden layer and 20 neurons in the hidden layer for radial basis function networks. 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 networks with radial basis function was selected as the optimal predictor for the behavior of the system.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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