A modified fuzzy c-regression model clustering algorithm for T-S fuzzy model identification

In this paper, a modified fuzzy c-regression model (FCRM) clustering algorithm for identification of Takagi-Sugeno (T-S) fuzzy model is proposed. The FCRM clustering algorithm has considerable sensitive to noise. To overcome this problem, a modified FCRM clustering algorithm is presented. This latte...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2011 8th International Multi-Conference on Systems, Signals and Devices s. 1 - 6
Hlavní autoři: Soltani, M, Aissaoui, B, Chaari, A, Ben Hmida, Faycal, Gossa, M
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2011
Témata:
Clustering algorithms
Data models
Fuzzy c-regression model
Fuzzy modeling
Mathematical model
Noise
Noise clustering algorithm
Noise measurement
Numerical models
Orthogonal least squares
Takagi-Sugeno fuzzy model
Training data
ISBN:9781457704130, 1457704137
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, a modified fuzzy c-regression model (FCRM) clustering algorithm for identification of Takagi-Sugeno (T-S) fuzzy model is proposed. The FCRM clustering algorithm has considerable sensitive to noise. To overcome this problem, a modified FCRM clustering algorithm is presented. This latter is based to adding a second regularization term in the alternative optimization process of FCRM. This regularization term is introduce in objective function in order to take in account the data are noisy. The parameters of the local linear models are identified based on orthogonal least squares (OLS). The proposed approach is demonstrated by means of the identification of nonlinear numerical examples.
ISBN:9781457704130
1457704137
DOI:10.1109/SSD.2011.5767365