CHOICE OF RBF MODEL STRUCTURE FOR PREDICTING GREENHOUSE INSIDE AIR TEMPERATURE

Abstract

The application of the radial basis function neural network to greenhouse inside air temperature modelling has been previously investigated by the authors. In those studies, the inside air temperature is modelled as a function of the inside relative humidity and of the outside temperature and solar radiation. Several training and learning methods were compared and the application of the Levenberg-Marquardt optimisation method was found to be the best way to determine the neural network parameters. A second-order model structure previously selected in the context of dynamic temperature models identification, was used. The model is intended to be incorporated in a real-time predictive greenhouse environmental control strategy. It is now relevant to question if the model structure used so far, selected in a different modelling framework, is the most correct in some sense. In this paper the usefulness of correlation-based model validity tests is addressed in order to answer the question mentioned above.