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https://open.uns.ac.rs/handle/123456789/12842
Nаziv: | Identification of Complex Systems Based on Neural and Takagi-Sugeno Fuzzy Model | Аutоri: | Kukolj, Dragan Levi E. |
Dаtum izdаvаnjа: | 1-јан-2004 | Čаsоpis: | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Sažetak: | The paper describes a neuro-fuzzy identification approach, which uses numerical data as a starting point. The proposed method generates a Takagi-Sugeno fuzzy model, characterized with transparency, high accuracy and a small number of rules. The process of self-organizing of the identification model consists of three phases: clustering of the input-output space using a self-organized neural network; determination of the parameters of the consequent part of a rule from over-determined batch least-squares formulation of the problem, using singular value decomposition algorithm; and on-line adaptation of these parameters using recursive least-squares method. The verification of the proposed identification approach is provided using four different problems: two benchmark identification problems, speed estimation for a dc motor drive, and estimation of the temperature in a tunnel furnace for clay baking. | URI: | https://open.uns.ac.rs/handle/123456789/12842 | ISSN: | 10834419 | DOI: | 10.1109/TSMCB.2003.811119 |
Nаlаzi sе u kоlеkciјаmа: | FTN Publikacije/Publications |
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