Neural network models in greenhouse air temperature prediction

Abstract

The adequacy of radial basis function neural networks to model the inside air temperature of a hydroponic greenhouse as a function of the outside air temperature and solar radiation, and the inside relative humidity, is addressed. As the model is intended to be incorporated in an environmental control strategy both off-line and on-line methods could be of use to accomplish this task. In this paper known hybrid off-line training methods and on-line learning algorithms are analyzed. An off-line method and its application to on-line learning is proposed. It exploits the linear-non-linear structure found in radial basis function neural networks.