All Outputs (4)

Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression (2022)
Conference Proceeding
Pekaslan, D., & Wagner, C. (2022). Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression. In IEEE World Congress on Computational Intelligence (IEEE WCCI2022). https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882840

The compositional representation of data and associated statistical approaches is a powerful framework for modelling and reasoning about quantities which reflect proportions of a whole. Recently, an increasing body of work has started exploring the a... Read More about Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression.

An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems (2021)
Conference Proceeding
Chen, C., Zhao, Y., Wagner, C., Pekaslan, D., & Garibaldi, J. M. (2021). An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz45933.2021.9494472

Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition app... Read More about An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems.

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels (2019)
Conference Proceeding
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2019). Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-7). https://doi.org/10.1109/FUZZ-IEEE.2019.8858800

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input... Read More about Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels.

Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems (2018)
Conference Proceeding
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018). Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ( 2960-2965). https://doi.org/10.1109/SMC.2018.00503

Real-world environments face a wide range of noise (uncertainty) sources and gaining insight into the level of noise is a critical part of many applications. While Non-Singleton Fuzzy Logic Systems (NSFLSs), in particular recently introduced advanced... Read More about Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems.