An adaptive fuzzy predictive control of nonlinear processes based on Multi-Kernel least squares support vector regression

In this paper, an adaptive fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi–Sugeno system based Multi-Kernel Least Squares Support Vector Regression (TS-LSSVR). The proposed adaptive TS-LSSVR strategy is constructed using a multi-kernel least squa...

Celý popis

Podrobná bibliografie

Vydáno v:	Applied soft computing Ročník 73; s. 572 - 590
Hlavní autoři:	Boulkaibet, I., Belarbi, K., Bououden, S., Chadli, M., Marwala, T.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.12.2018
Témata:	Fixed-budget kernel recursive least-squares Fuzzy c-means clustering Generalized predictive control Least square support vector regression Takagi–Sugeno fuzzy system Takagi–Sugeno fuzzy system Least square support vector regression Fixed-budget kernel recursive least-squares Fuzzy c-means clustering Generalized predictive control
ISSN:	1568-4946, 1872-9681
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!