Evolutionary multiobjective design of radial basis function networks for greenhouse environmental control

Ferreira, P. M.; Ruano, Antonio; Fonseca, C. M.

http://hdl.handle.net/10400.1/2344

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Authors

Ferreira, P. M.

Ruano, Antonio

Fonseca, C. M.

Abstract(s)

In this work a multiobjective genetic algorithm is applied to the identi cation of radial basis function neural network coupled models of humidity and temperature in a greenhouse. Models are built as one-step-ahead predictors and then used iteratively to produce long term predictions. The number of neurons and input terms used in both models de ne the search space. Two combinations of performance and complexity criteria are used to steer the selection of model structures, resulting in distinct sets of solutions. It is shown that minimisation of one-step-ahead prediction errors negatively in uences long term prediction performance. Long term prediction results are presented for a pair of models selected from sets of models obtained in the experiments.

Keywords

Genetic Algorithms Greenhouse Environmental Control Radial Basis Functions Temperature Prediction Humidity Prediction

URI

http://hdl.handle.net/10400.1/2344

Citation

Ferreira, P. M.; Ruano, A. E.; Fonseca, C. M. Evolutionary multiobjective design of radial basis function networks for greenhouse environmental control, Trabalho apresentado em World Congress, In Proceedings of the 16th IFAC World Congress, 2005, Prague, 2005.