FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language

被引:2
|
作者
Chen, Chao [1 ]
Razak, Tajul Rosli [1 ,2 ]
Garibaldi, Jonathan M. [1 ]
机构
[1] Univ Nottingham, Sch Comp Sci, Lab Uncertainty Data & Decis Making LUCID, Intelligent Modelling & Anal Grp IMA, Nottingham, England
[2] Univ Teknol MARA, Fac Comp & Math Sci, Perlis Branch, Shah Alam, Malaysia
来源
2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2020年
关键词
INTERVAL TYPE-2; SYSTEMS; REDUCTION; DESIGN; SETS;
D O I
10.1109/fuzz48607.2020.9177780
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:8
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