JACIII Vol.15 No.3 pp. 345-350
doi: 10.20965/jaciii.2011.p0345


Unnormalized Interval Type-2 TSK Fuzzy Logic System Design Based on Convexity and Sample Data

Tiechao Wang*,** and Jianqiang Yi*

*Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Haidian District, Beijng 100190, China

**Liaoning University of Technology, Jinzhou, Liaoning 121001, China

October 28, 2010
December 21, 2010
May 20, 2011
convexity, interval type-2 fuzzy logic system, least squares algorithm, prior knowledge
Prior knowledge of convexity is encoded into a Single-Input Single-Output (SISO) unnormalized interval type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System (FLS) such that the system converges to a given convex target function. After giving sufficient conditions to guarantee convexity with respect to inputs, we show how to combine convexity with Unnormalized Interval Type-2 TSK FLSs (UIT2FLSs) to design convex fuzzy systems enabling derived systems to approach the target function. A simulation example demonstrates the usefulness of convexity and the advantages of UIT2FLSs in the presence of noise.
Cite this article as:
T. Wang and J. Yi, “Unnormalized Interval Type-2 TSK Fuzzy Logic System Design Based on Convexity and Sample Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.3, pp. 345-350, 2011.
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