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Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate
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Author(s): M. Outanoute (Sensors Electronic & Instrumentation Group, Physics Department, Faculty of Sciences, Moulay Ismaïl University, Meknes, Morocco), A. Lachhab (Modelling, Systems Control and Telecommunications Team, Department of Electrical Engineering, High School of Technology, Moulay Ismaïl University, Meknes, Morocco), A. Selmani (Sensors Electronic & Instrumentation Group, Physics Department, Faculty of Sciences, Moulay Ismaïl University, Meknes, Morocco), H. Oubehar (Sensors Electronic & Instrumentation Group, Physics Department, Faculty of Sciences, Moulay Ismaïl University, Meknes, Morocco), A. Snoussi (Modelling, Systems Control and Telecommunications Team, Department of Electrical Engineering, High School of Technology, Moulay Ismaïl University, Meknes, Morocco), M. Guerbaoui (Modelling, Systems Control and Telecommunications Team, Department of Electrical Engineering, High School of Technology, Moulay Ismaïl University, Meknes, Morocco), A. Ed-dahhak (Modelling, Systems Control and Telecommunications Team, Department of Electrical Engineering, High School of Technology, Moulay Ismaïl University, Meknes, Morocco)and B. Bouchikhi (Sensors Electronic & Instrumentation Group, Physics Department, Faculty of Sciences, Moulay Ismaïl University, Meknes, Morocco)
Copyright: 2022
Pages: 12
Source title:
Research Anthology on Artificial Neural Network Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-2408-7.ch063
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Abstract
In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.
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