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Uncertainty modeling of improved fuzzy functions with evolutionary systems
Asli Celikyilmaz1, I Burhan Turksen
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1776, USA. asli@eecs.berkeley.edu
Summary
This study introduces an evolutionary type-2 fuzzy function system (ET2FF) for enhanced uncertainty modeling. The ET2FF method improves generalization and robustness in real-world applications.
Area of Science:
- Computational Intelligence
- Fuzzy Systems Engineering
- Machine Learning
Background:
- Type-1 fuzzy inference systems (FISs) with fuzzy functions show promise but are limited by parameter uncertainty.
- Traditional FIS rely on rule bases, which can be less adaptable than fuzzy function approaches.
Purpose of the Study:
- To introduce a novel evolutionary type-2 fuzzy function system (ET2FF) for robust uncertainty modeling.
- To address the performance limitations of existing fuzzy systems caused by uncertain parameters and structures.
Main Methods:
- A three-phase learning strategy is implemented to capture uncertainties in fuzzy function systems.
- Improved fuzzy clustering identifies hidden data structures.
- A genetic learning algorithm optimizes interval type-2 fuzzy sets for uncertainty intervals.
Main Results:
- The ET2FF architecture was evaluated on diverse real-life applications, including manufacturing and financial modeling.
- The proposed method demonstrates comparable or superior generalization performance and robustness against earlier FIS.
- The system effectively captures uncertainties in both parameters and fuzzy function structures.
Conclusions:
- The ET2FF offers a powerful framework for uncertainty modeling in complex systems.
- This approach enhances the reliability and performance of fuzzy inference systems.
- ET2FF provides a robust solution for real-world applications sensitive to uncertainty.
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