The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A fuzzy-logic-based approach to qualitative modeling
Abstract
This paper discusses a general approach to quali- tative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action.
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
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Cited by
Fuzzy logic systems for engineering: a tutorial
Mendel, J. M. (1995) · Proceedings of the IEEE · doi:10.1109/5.364485 · Cited by 2,110
Validity-guided (re)clustering with applications to image segmentation
Bensaid, A. M., Hall, L. O., Bezdek, J. C., Clarke, L. P., Silbiger, M. L., Arrington, J. A., & Murtagh, R. F. (1996) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.493905 · Cited by 425
Ishibuchi, H., Murata, T., & Türkşen, I. B. (1997) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(96)00098-x · Cited by 379
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems
Kim, J., & Kasabov, N. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00067-2 · Cited by 351
Kaab, A., Sharifi, M., Mobli, H., Nabavi-Pelesaraei, A., & Chau, K.-W. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.02.004 · Cited by 349
Metadata
- Authors
- Sugeno, M., & Yasukawa, T.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 1993-02
- Volume
- 1
- Issue
- 1
- Pages
- 7
- ISSN
- 1063-6706 (ISSN-L)
- OpenAlex
- W2159265133
- Cited by
- 2,411
- References
- 1
- FWCI
- 59.25
- 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: A fuzzy-logic-based approach to qualitative modeling. https://app.cochat.ai/papers/10.1109/tfuzz.1993.390281
- The paper
- Sugeno, M., & Yasukawa, T. (1993). A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/tfuzz.1993.390281
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.
