Neural network models in greenhouse environmental control

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 hybrid off-line training methods and on-line learning algorithms are analysed. An off-line method and its application to on-line learning is presented. It exploits the linear-nonlinear structure found in radial basis function neural networks