Fuzzy identification of a greenhouse
Applied Soft Computing
Constructing neural network sediment estimation models using a data-driven algorithm
Mathematics and Computers in Simulation
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
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Greenhouse operation and inside climate strongly depend on the outside weather. This implies that at least a year of data collection is required to cover the whole operational domain. Greenhouse-climate models calibrated with data limited to only a small region of the operating domain (weather and control), may therefore, produce erroneous predictions when applied to unfamiliar conditions.A comparison is made between the performance of three types of models trained with several seasonal subsets of data: (1) black-box (BB) sigmoid neural network (NN) trained only with in situ data, (2) hybrid physical-RBF (radial basis function) model, and (3) sigmoid neural network trained with a combination of in situ data and synthetic data generated with a physical model (termed 'prior-K sigmoid model').The BB sigmoid model gives the best predictions within the training domain, but performs very badly outside it. On the other hand, the hybrid and prior-K sigmoid models produce useful predictions over the whole operating domain, although they are slightly less accurate within the training domain.