Ferreira, P. M.Faria, E. A.Ruano, Antonio2013-02-132013-02-132002Ferreira, P. M.; Faria, E. A.; Ruano, A. E. Neural network models in greenhouse air temperature prediction, Neurocomputing, 43, 1-4, 51-75, 2002.0925-2312AUT: ARU00698;http://hdl.handle.net/10400.1/2343The 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 botho--line and on-line methods could be of use to accomplish this task. In this paper known hybrid o--line training methods and on-line learning algorithms are analyzed. An o--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.engRadial basis functionsNeural networksGreenhouse environmental controlModellingjournal article2013-01-27http://dx.doi.org/10.1016/S0925-2312(01)00620-8