NDLI: Genetic assisted selection of RBF model structures for greenhouse inside air temperature prediction

Content Provider	IEEE Xplore Digital Library
Author	Ferreira, P.M. Ruano, A.E. Fonseca, C.M.
Copyright Year	2003
Description	Author affiliation: Fac. of Sci. & Technol., Univ. of Algarve, Faro, Portugal (Ferreira, P.M.; Ruano, A.E.; Fonseca, C.M.)
Abstract	This paper presents results on the application of Multi-Objective Genetic Algorithms to the selection of Radial Basis Function Neural Networks structures. The neural networks are to be incorporated in a real-time predictive greenhouse environmental control strategy, as predictors of the inside air temperature. Previous research conducted by the authors modelled the inside air temperature as a function of the inside relative humidity and of the outside temperature and solar radiation. A second-order model structure previously selected in the context of dynamic temperature models identification was used. Several training and learning methods were compared, and the application of the Levenberg-Marquardt optimisation method was found to be the best way to determine the neural network parameters. The application of correlation-based model-validity tests revealed that the validity of such a second-order model structure could be manually improved after inspection of the tests results. Both network performance and validity are certainly affected by the number of neurons, the input variables considered and the time delays used. As the number of alternatives is huge, Multi-Objective Genetic Algorithms are applied here to the selection of network inputs and number of neurons.
Starting Page	576
Ending Page	581
File Size	461780
Page Count	6
File Format	PDF
ISBN	078037729X
DOI	10.1109/CCA.2003.1223500
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-06-25
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Genetic algorithms Neural networks Context modeling Testing Neurons Radial basis function networks Temperature control Humidity Solar radiation
Content Type	Text
Resource Type	Article

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