Publicou 18 edições por ano
ISSN Imprimir: 1064-2285
ISSN On-line: 2162-6561
IF:
1.7
5-Year IF:
1.4
Immediacy Index:
0.6
Eigenfactor:
0.00072
JCI:
0.43
SJR:
0.318
SNIP:
0.568
CiteScore™::
3.5
H-Index:
28
Indexed in
Predictive Control of a Humidifying Process Modelled on Reproducing Kernel Hilbert Spaces
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RESUMO
This paper focuses on the Model Based Predictive Control (MBPC) of the humidifying process inside a drying blower. This process is known to have a nonlinear behavior. To synthesize the MBP control, we used a Reproducing Kernel Hilbert Space (RKHS) model with reduced complexity. The identification of this model is carried in a black box context with no a priori information needed, using the Statistical Learning Techniques (SLT). This model is linear with respect to its parameters and copes well with the nonlinear systems approximation problems. The proposed algorithm is used to regulate the humidity inside a drying blower.
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Souilem Nadia, Elaissi Ilyes, Messaoud Hassani, New method of RKHS model complexity reduction, The International Journal of Advanced Manufacturing Technology, 82, 5-8, 2016. Crossref
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Souilem Nadia, Elaissi Ilyes, Messaoud Hassani, On the use of KPCA pre-filtering for KCCA method, The International Journal of Advanced Manufacturing Technology, 91, 9-12, 2017. Crossref