Optimization of Expert Fuzzy Systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00384807" target="_blank" >RIV/68407700:21110/25:00384807 - isvavai.cz</a>
Result on the web
<a href="https://concrete.fsv.cvut.cz/phdworkshop/" target="_blank" >https://concrete.fsv.cvut.cz/phdworkshop/</a>
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Optimization of Expert Fuzzy Systems
Original language description
Fuzzy inference expert systems (FIS) provide a powerful framework for modeling complex, nonlinear, and uncertain systems by emulating human-like reasoning through fuzzy rules and membership functions. Tuning these systems to fit measured data remains challenging, especially when their interpretability must be preserved. This paper reviews existing FIS optimization techniques, including MATLAB’s tunefis and ANFIS. It then introduces three new data-driven methods for interpretable FIS tuning: (1) input membership function adjustment via inverse optimization, (2) gradient-based tuning of rule consequents, and (3) Bayesian parameter calibration accounting for measurement uncertainty. Each method is designed to enhance system performance while upholding the logical and semantic structure of fuzzy models. The proposed methods are then applied to a practical example and compared.
Czech name
—
Czech description
—

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)