2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 1 , PP: 253-263, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
María F. Molina 1 * , Secundino Marrero 2
Doi: https://doi.org/10.54216/FPA.160118
Agricultural systems, such as greenhouses, can be used to control environmental factors, such as temperature and humidity, to increase output by employing traditional automation techniques. The advancement of science has resulted in the utilization of mathematical models to understand the behavior of data by analyzing its variability. The objective of this project is to validate a method for controlling temperature and humidity in controlled experimental environments using artificial intelligence and Neutrosophy. The transfer functions obtained from temperature and humidity readings gathered via a SCADA system are utilized. Neutrosophic numbers are used to adjust the temperature and humidity values based on the experimental conditions of the greenhouse, indicating the optimal, important, and sensitive ranges. The control system being investigated employs NARMA-L2 neural networks that belong to the multilayer perception category. This facilitates efficient system administration and showcases outstanding performance in simulations conducted across several temperature and humidity scenarios. The observed errors consistently remain below 5% and any instances of exceeding this threshold are insignificant.
NARMA-L2, neutrosophy , nonlinear models , temperature , humidity.
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