The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Semantic constraints for membership function optimization
Abstract
The optimization of fuzzy systems using bio-inspired strategies, such as neural network learning rules or evolutionary optimization techniques, is becoming more and more popular. In general, fuzzy systems optimized in such a way cannot provide a linguistic interpretation, preventing us from using one of their most interesting and useful features. This paper addresses this difficulty and points out a set of constraints that when used within an optimization scheme obviate the subjective task of interpreting membership functions. To achieve this a comprehensive set of semantic properties that membership functions should have is postulated and discussed. These properties are translated in terms of nonlinear constraints that are coded within a given optimization scheme, such as backpropagation. Implementation issues and one example illustrating the importance of the proposed constraints are included.
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
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
Cited by
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Gacto, M. J., Alcalá, R., & Herrera, F. (2011) · Information Sciences · doi:10.1016/j.ins.2011.02.021 · Cited by 565
GA-fuzzy modeling and classification: complexity and performance
Setnes, M., & Roubos, H. (2000) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.873575 · Cited by 473
Cordón, O. (2011) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2011.03.004 · Cited by 347
SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network
de Jesus Rubio, J. (2009) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2009.2029569 · Cited by 346
Fazzolari, M., Alcala, R., Nojima, Y., Ishibuchi, H., & Herrera, F. (2013) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2012.2201338 · Cited by 296
Metadata
- Authors
- de Oliveira, J. V.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- Published
- 1999
- Volume
- 29
- Issue
- 1
- Pages
- 128-138
- ISSN
- 1083-4427 (ISSN-L)
- OpenAlex
- W2128069141
- Cited by
- 288
- References
- 1
- FWCI
- 10.99
- 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: Semantic constraints for membership function optimization. https://app.cochat.ai/papers/10.1109/3468.736369
- The paper
- de Oliveira, J. V. (1999). Semantic constraints for membership function optimization. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. https://doi.org/10.1109/3468.736369
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.