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Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
Abstract
Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. An initial fuzzy rule system is generated based on the conclusion that optimal fuzzy rules cover extrema. Redundant rules are removed based on a fuzzy similarity measure. Then, the structure and parameters of the fuzzy system are optimized using a genetic algorithm and the gradient method. During optimization, rules that have a very low firing strength are deleted. Finally, interpretability of the fuzzy system is improved by fine training the fuzzy rules with regularization. The resulting fuzzy system generated by this method has the following distinct features: (1) the fuzzy system is quite simplified; (2) the fuzzy system is interpretable; and (3) the dependencies between the inputs and the output are clearly shown. This method has successfully been applied to a system that has 11 inputs and one output with 20 000 training data and 80 000 test data.
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Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Cited by
Ten years of genetic fuzzy systems: current framework and new trends
Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., & Magdalena, L. (2004) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(03)00111-8 · Cited by 819
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
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
Jin, Y., & Sendhoff, B. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2008.919172 · Cited by 455
Self-citationAlcala-Fdez, J., Alcala, R., & Herrera, F. (2011) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2011.2147794 · Cited by 371
Cordón, O. (2011) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2011.03.004 · Cited by 347
Metadata
- Authors
- Jin, Y.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 2000-04
- Volume
- 8
- Issue
- 2
- Pages
- 212-221
- ISSN
- 1063-6706 (ISSN-L)
- OpenAlex
- W2119893066
- Cited by
- 480
- References
- 1
- FWCI
- 22.07
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
