The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Generating fuzzy rules by learning from examples
Abstract
A general method is developed to generate fuzzy rules from numerical data. The method consists of five steps: divide the input and output spaces of the given numerical data into fuzzy regions; generate fuzzy rules from the given data; assign a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; create a combined fuzzy rule base based on both the generated rules and linguistic rules of human experts; and determine a mapping from input space to output space based on the combined fuzzy rule base using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. Applications to truck backer-upper control and time series prediction problems are presented.>
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Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Cited by
Data mining in manufacturing: a review based on the kind of knowledge
Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-008-0145-x · Cited by 494
FURIA: an algorithm for unordered fuzzy rule induction
Hühn, J., & Hüllermeier, E. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0131-8 · Cited by 441
The 2017 IEEE International Conference on Fuzzy Systems
(2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2016.2545826 · Cited by 308
Fuzzy logic applied to opinion mining: A review
Serrano-Guerrero, J., Romero, F. P., & Olivas, J. A. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107018 · Cited by 141
Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks
de Campos Souza, P. V., Guimarães, A. J., Rezende, T. S., Silva Araujo, V. J., & Araujo, V. S. (2020) · AI · doi:10.3390/ai1010005 · Cited by 16
Metadata
- Authors
- Wang, L.-X., & Mendel, J. M.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1992
- Volume
- 22
- Issue
- 6
- Pages
- 1414-1427
- ISSN
- 0018-9472 (ISSN-L)
- OpenAlex
- W2050288270
- Cited by
- 2,894
- References
- 2
- FWCI
- 29.51
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, L.-X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/21.199466
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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.
