A recently published paper investigates the identification and modeling of a greenhouse's climate using real climate data from a greenhouse installed in the LAPER laboratory in Tunisia.
"The objective of this paper is to propose a solution to the problem of nonlinear time-variant inputs and outputs of greenhouse internal climate," the researchers explain.
Combining fuzzy logic technique with Least Mean Squares (LMS), a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model-based algorithm.
"This method has the following advantages: it allows modifying the hierarchical structures by adding or removing a sub-model or rules at any time without the need to repeat the whole identification process and to use all the collected data," they add. "These sub-models have similar correspondences in the physical modeling, which represent the contributions of the process mechanisms involved in the global process."
Haj Hamad, Imen & Chouchaine, Amine & Bouzaouache, Hajer. (2021). A Takagi-Sugeno Fuzzy Model for Greenhouse Climate. Engineering, Technology & Applied Science Research. 11. 7424-7429. 10.48084/etasr.4291.