The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Input selection for ANFIS learning
Abstract
We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using adaptive neuro-fuzzy inference systems (ANFIS). The method is tested on two real-world problems: the nonlinear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identification using the Box and Jenkins gas furnace data.
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
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Cited by
Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
Al-Hmouz, A., Shen, J., Al-Hmouz, R., & Yan, J. (2012) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2011.36 · Cited by 286
Missing data imputation through the use of the random forest algorithm
Pantanowitz, A., & Marwala, T. (2009) · Advances in Intelligent and Soft Computing · doi:10.1007/978-3-642-03156-4_6 · Cited by 106
Prediction of antimicrobial peptides based on the adaptive neuro‐fuzzy inference system application
Fernandes, F. C., Rigden, D. J., & Franco, O. L. (2012) · Peptide Science · doi:10.1002/bip.22066 · Cited by 67
Metadata
- Authors
- Jang, J.-S. R.
- Venue
- Proceedings of IEEE 5th International Fuzzy Systems
- Publisher
- IEEE
- Published
- 2002-12-24
- Volume
- 2
- Pages
- 1493-1499
- ISSN
- 1098-7584 (ISSN-L)
- OpenAlex
- W2103499201
- Cited by
- 378
- References
- 1
- FWCI
- 14.14
- Type
- conference paper
- 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: Input selection for ANFIS learning. https://app.cochat.ai/papers/10.1109/fuzzy.1996.552396
- The paper
- Jang, J.-S. R. (2002). Input selection for ANFIS learning. Proceedings of IEEE 5th International Fuzzy Systems. https://doi.org/10.1109/fuzzy.1996.552396
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.
