Pham, B.H. and Ha, H.T. and Ngo, L.T. (2012) Learning rule for TSK fuzzy logic systems using interval type-2 fuzzy subtractive clustering. In: 9th International Conference on Simulated Evolution and Learning, SEAL 2012, 16 December 2012 through 19 December 2012, Hanoi.
Learning rule for TSK fuzzy logic systems using interval type-2 fuzzy subtractive clustering.pdf
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Abstract
The paper deals with an approach to model TSK fuzzy logic systems (FLS), especially interval type-2 TSK FLS, using interval type-2 fuzzy subtractive clustering (IT2-SC). The IT2-SC algorithm is combined with least square estimation (LSE) algorithms to pre-identify a type-1 FLS form from input/output data. Then, an interval type-2 TSK FLS can be obtained by considering the membership functions of its existed type-1 counterpart as primary membership functions and assigning uncertainty to cluster centroids, standard deviation of Gaussian membership functions and consequence parameters. Results is shown in comparison with the approach based on type-1 subtractive clustering algorithm. © 2012 Springer-Verlag.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Faculties > Faculty of Information Technology |
| Identification Number: | 10.1007/978-3-642-34859-4_43 |
| Uncontrolled Keywords: | Cluster centroids; Fuzzy logic system; Fuzzy subtractive clustering; Gaussian membership function; Input/output datum; Learning rules; Least square estimation; Standard deviation; Subtractive clustering; Subtractive clustering algorithms; TSK fuzzy logic; TSK models; Type-2 fuzzy set; Fuzzy logic; Fuzzy sets; Clustering algorithms |
| Additional Information: | Conference code: 94550. Language of original document: English. |
| URI: | http://eprints.lqdtu.edu.vn/id/eprint/10086 |
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