Hybrid MLP-RBF model structure for short-term internal temperature prediction in greenhouse environments

Peter Eredics, Tadeusz P. Dobrowiecki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A wide variety of greenhouse temperature models have been proposed in the literature in the previous years. This paper proposes a hybrid modeling method incorporating a multilayer perceptron neural network and a radial basis function neural network aimed to be more accurate on input regions not covered by training data. The results show that the proposed method has better performance compared to the original physical-neural hybrid model if the input values are not far from the input range of the values used for training.

Original languageEnglish
Title of host publicationCINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings
Pages377-380
Number of pages4
DOIs
Publication statusPublished - Dec 1 2013
Event14th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2013 - Budapest, Hungary
Duration: Nov 19 2013Nov 21 2013

Publication series

NameCINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings

Other

Other14th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2013
CountryHungary
CityBudapest
Period11/19/1311/21/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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    Eredics, P., & Dobrowiecki, T. P. (2013). Hybrid MLP-RBF model structure for short-term internal temperature prediction in greenhouse environments. In CINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings (pp. 377-380). [6705225] (CINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings). https://doi.org/10.1109/CINTI.2013.6705225