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Hybrid MLP-RBF model structure for short-term internal temperature prediction in greenhouse environments

A wide variety of greenhouse temperature models have been proposed in the literature in the previous years. This paper proposes a hybrid modeling method incorporating a multilayer perceptron neural network and a radial basis function neural network aimed to be more accurate on input regions not covered by training data. The results show that the proposed method has better performance compared to the original physical-neural hybrid model if the input values are not far from the input range of the values used for training.