Predicting the Greenhouse Inside Air Temperature with RBF Neural Networks

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. A second-order model structure previously selected in the context of dynamic temperature models identification, is used. Several training and learning methods were compared and the application of the Levenberg-Marquardtoptimisation method was found to be the best way to determine the neural network parameters. Such a type of model is intended to be incorporated in a real-time predictive greenhouse environmental control strategy, which implies that prediction horizons greater than one time step will be necessary. In this paper the radial basis function neural network will be compared to conventional auto-regressive with exogenous inputs models, on the prediction of the greenhouse inside air temperature, considering prediction horizons greater than one time step.