Constraints Always Satisfied Parameters (CASPs) for Fuzzy Sets Optimisation

The design of membership functions in fuzzy systems often requires satisfying domain, semantic, and relational constraints. Existing methods, while effective at enforcing parameter bounds, often lack flexibility or fail to address complex relational constraints. To overcome these limitations, this p...

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Vydáno v:IEEE transactions on fuzzy systems s. 1 - 10
Hlavní autoři: Chen, Chao, Mendel, Jerry M., Garibaldi, Jonathan M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
CAASPs
CASPs
Constraints Always Satisfied Parameters
Fuzzy set optimisation
Fuzzy sets
Fuzzy system
Fuzzy systems
Membership function design
Optimization
Reproducibility of results
Semantics
Shape
System performance
Systematics
Uncertainty
Vectors
ISSN:1063-6706, 1941-0034
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Shrnutí:The design of membership functions in fuzzy systems often requires satisfying domain, semantic, and relational constraints. Existing methods, while effective at enforcing parameter bounds, often lack flexibility or fail to address complex relational constraints. To overcome these limitations, this paper introduces the Constraints Always Satisfied Parameters (CASPs) framework, which inherently satisfies constraints during optimisation. Three variants of the CASPs are proposed, each balancing design flexibility, performance, and interpretability differently. Experimental evaluations on the Electricity and Laser datasets demonstrate consistent constraint satisfaction across all runs, with CASPs-Single prioritising interpretability, CASPs-Free excelling in RMSE performance, and CASPs-Adapted offering a balanced approach. The results highlight the potential of CASPs to enhance the design and optimisation of fuzzy systems.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2025.3630481