Closed form fuzzy interpolation with interval type-2 fuzzy sets

Longzhi Yang; Chengyuan Chen; Nanlin Jin; Xin Fu; Qiang Shen

doi:10.1109/FUZZ-IEEE.2014.6891643

Closed form fuzzy interpolation with interval type-2 fuzzy sets

Longzhi Yang, Chengyuan Chen, Nanlin Jin, Xin Fu, Qiang Shen

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

18 Citations (Scopus)

Abstract

Fuzzy rule interpolation enables fuzzy inference with sparse rule bases by interpolating inference results, and may help to reduce system complexity by removing similar (often redundant) neighbouring rules. In particular, the recently proposed closed form fuzzy interpolation offers a unique approach which guarantees convex interpolated results in a closed form. However, the difficulty in defining the required precise-valued membership functions still poses significant restrictions over the applicability of this approach. Such limitations can be alleviated by employing type-2 fuzzy sets as their membership functions are themselves fuzzy. This paper extends the closed form fuzzy rule interpolation using interval type-2 fuzzy sets. In this way, as illustrated in the experiments, closed form fuzzy interpolation is able to deal with uncertainty in data and knowledge with more flexibility.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2184-2191
Number of pages	8
ISBN (Electronic)	9781479920723
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2014.6891643
Publication status	Published - 4 Sept 2014
Externally published	Yes
Event	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Publication series

Name	IEEE International Conference on Fuzzy Systems
ISSN (Print)	1098-7584

Conference

Conference	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14

Keywords

Fuzzy rule interpolation
closed form interpolation
interval type-2 fuzzy sets

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10.1109/FUZZ-IEEE.2014.6891643

Cite this

Yang, L., Chen, C., Jin, N., Fu, X., & Shen, Q. (2014). Closed form fuzzy interpolation with interval type-2 fuzzy sets. In Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE (pp. 2184-2191). Article 6891643 (IEEE International Conference on Fuzzy Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2014.6891643

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author = "Longzhi Yang and Chengyuan Chen and Nanlin Jin and Xin Fu and Qiang Shen",

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Yang, L, Chen, C, Jin, N, Fu, X & Shen, Q 2014, Closed form fuzzy interpolation with interval type-2 fuzzy sets. in Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE., 6891643, IEEE International Conference on Fuzzy Systems, Institute of Electrical and Electronics Engineers Inc., pp. 2184-2191, 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/FUZZ-IEEE.2014.6891643

Closed form fuzzy interpolation with interval type-2 fuzzy sets. / Yang, Longzhi; Chen, Chengyuan; Jin, Nanlin et al.
Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2184-2191 6891643 (IEEE International Conference on Fuzzy Systems).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AB - Fuzzy rule interpolation enables fuzzy inference with sparse rule bases by interpolating inference results, and may help to reduce system complexity by removing similar (often redundant) neighbouring rules. In particular, the recently proposed closed form fuzzy interpolation offers a unique approach which guarantees convex interpolated results in a closed form. However, the difficulty in defining the required precise-valued membership functions still poses significant restrictions over the applicability of this approach. Such limitations can be alleviated by employing type-2 fuzzy sets as their membership functions are themselves fuzzy. This paper extends the closed form fuzzy rule interpolation using interval type-2 fuzzy sets. In this way, as illustrated in the experiments, closed form fuzzy interpolation is able to deal with uncertainty in data and knowledge with more flexibility.

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Closed form fuzzy interpolation with interval type-2 fuzzy sets

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