The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Type-2 fuzzy logic systems
Abstract
We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
The concept of a linguistic variable and its application to approximate reasoning-III
Zadeh, L. A. (1975) · Information Sciences · doi:10.1016/0020-0255(75)90017-1 · Cited by 12,139
Fuzzy Sets and Systems - Theory and Applications
(1980) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(09)x6008-x · Cited by 6,383
Fuzzy Sets and Fuzzy Logic: Theory and Applications
Klir, G. J., & Yuan, B. (1995) · Medical Entomology and Zoology · Cited by 6,240
Pattern classification and scene analysis
Chien, Y. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100577 · Cited by 3,398
Adaptive Fuzzy Systems and Control: Design and Stability Analysis
Wang, L. (1994) · Medical Entomology and Zoology · Cited by 2,736
DUBOIS, D., & PRADE, H. (1978) · International Journal of Systems Science · doi:10.1080/00207727808941724 · Cited by 2,696
An Introduction to Fuzzy Control
Driankov, D., Hellendoorn, H., & Reinfrank, M. (1993) · Springer eBooks · doi:10.1007/978-3-662-11131-4 · Cited by 2,330
Fuzzy logic systems for engineering: a tutorial
Mendel, J. M. (1995) · Proceedings of the IEEE · doi:10.1109/5.364485 · Cited by 2,110
Self-citationInterval type-2 fuzzy logic systems: theory and design
Liang, Q., & Mendel, J. M. (2000) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.873577 · Cited by 2,000
Self-citation
Cited by
On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers
Wu, D. (2012) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2012.2186818 · Cited by 339
Geometric Type-1 and Type-2 Fuzzy Logic Systems
Coupland, S., & John, R. (2007) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2006.889764 · Cited by 316
General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial
Mendel, J. M. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2286414 · Cited by 311
Self-citationThe 2017 IEEE International Conference on Fuzzy Systems
(2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2016.2545826 · Cited by 308
Türk, S., Deveci, M., Özcan, E., Canıtez, F., & John, R. (2021) · Information Sciences · doi:10.1016/j.ins.2020.08.076 · Cited by 98
Metadata
- Authors
- Karnik, N. N., Mendel, J. M., & Liang, Q.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 1999
- Volume
- 7
- Issue
- 6
- Pages
- 643-658
- ISSN
- 1063-6706 (ISSN-L)
- OpenAlex
- W2105234323
- Cited by
- 1,643
- References
- 27
- FWCI
- 12.21
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Type-2 fuzzy logic systems. https://app.cochat.ai/papers/10.1109/91.811231
- The paper
- Karnik, N. N., Mendel, J. M., & Liang, Q. (1999). Type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/91.811231
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.