Application of computational intelligence methods to greenhouse environmental modelling

In order to implement a model-based predictive control methodology for a research greenhouse several predictive models are required. This paper presents the modelling framework and results about the models that were identified. RBF neural networks are used as non-linear auto-regressive and non-linear auto-regressive with exogenous inputs models. The networks parameters are determined using the Levenberg-Marquardt optimisation method and their structure is selected by means of multi-objective genetic algorithms. By network structure we refer to the number of neurons of the networks, the input variables and for each variable considered its lagged input terms. Two types of models were identified: process models (greenhouse climate) and external disturbances (external weather). Pseudo-random binary signals were employed to generate control input commands for the greenhouse actuators, in order to build input/output data sets suitable for the process models identification. The final model arrangement consists of four interconnected models, two of which are coupled, providing greenhouse climate and external weather long term predictions.