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An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems

Chen, Chao; Zhao, Yu; Wagner, Christian; Pekaslan, Direnc; Garibaldi, Jonathan M.


Transitional Assistant Professor

Yu Zhao


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 approaches to modelling the interaction between the non-singleton input and the antecedent fuzzy sets enable the efficient handling of uncertainty without requiring changes in a system's rule base, with benefits both in terms of performance and interpretability. As thus far few current software toolkit support non-singleton fuzzy systems, this paper presents an extension of the FuzzyR toolbox, which is a freely available R package on CRAN, for non-singleton fuzzy logic systems. The updated toolbox enables a non-singleton model to be conveniently built from scratch, or for existing singleton fuzzy logic systems built using FuzzyR to be converted easily. Predefined operations include fuzzification of crisp inputs (e.g. into Gaussian membership functions), and a variety of composition approaches for computing rules' firing-strengths, based on the standard, centroid-based, and similarity-based methods. It is also possible to include user-defined options for these abovementioned methods, without the need to modify (or update) the FuzzyR toolbox itself. In this paper, detailed introductions for the new non-singleton features of the toolkit are presented, complete with code samples in R to facilitate adoption both within and beyond the community. Further, the paper presents a series of validation experiments, replicating a recent empirical analysis of non-singleton fuzzy logic systems in the context of time-series prediction with different levels of noise.


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).

Conference Name 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Conference Location Luxembourg
Start Date Jul 11, 2021
End Date Jul 14, 2021
Online Publication Date Aug 5, 2021
Publication Date Jul 11, 2021
Deposit Date Dec 2, 2021
Publisher Institute of Electrical and Electronics Engineers
Book Title 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN 9781665444088
Public URL
Publisher URL