CHAO CHEN Chao.Chen@nottingham.ac.uk
Transitional Assistant Professor
FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language
Chen, Chao; Razak, Tajul Rosli; Garibaldi, Jonathan M.
Authors
Tajul Rosli Razak
Prof. JONATHAN GARIBALDI JON.GARIBALDI@NOTTINGHAM.AC.UK
Provost and Pvc Unnc
Abstract
This paper presents an R package FuzzyR which is an extended fuzzy logic toolbox for the R programming language. FuzzyR is a continuation of the previous Fuzzy R toolboxes such as FuzzyToolkitUoN. Whilst keeping existing functionalities of the previous toolboxes, the main extension in the FuzzyR toolbox is the capability of optimising type-1 and interval type-2 fuzzy inference systems based on an extended ANFIS architecture. An accuracy function is also added to provide performance indicators featuring eight alternative accuracy measures, including a new measure UMBRAE. In addition, graphical user interfaces have been provided so that the properties of a fuzzy inference system can be visualised and manipulated, which is particularly useful for teaching and learning. Note that this paper illustrates some of the new features of the FuzzyR toolbox, but does not provide a complete list of all functions available. More details about the new features of FuzzyR and a complete description of all functions can be found in the manual of the toolbox.
Citation
Chen, C., Razak, T. R., & Garibaldi, J. M. (2020). FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-8). https://doi.org/10.1109/fuzz48607.2020.9177780
Conference Name | 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
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Conference Location | Glasgow, UK |
Start Date | Jul 19, 2020 |
End Date | Jul 24, 2020 |
Online Publication Date | Aug 26, 2020 |
Publication Date | 2020-07 |
Deposit Date | Dec 2, 2021 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 1-8 |
Book Title | 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
ISBN | 9781728169330 |
DOI | https://doi.org/10.1109/fuzz48607.2020.9177780 |
Public URL | https://nottingham-repository.worktribe.com/output/6847317 |
Publisher URL | https://ieeexplore.ieee.org/document/9177780 |
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