Software

JuzzyOnline

JuzzyOnline is a browser-based toolkit for the design and execution of type-1, interval and general type-2 fuzzy logic systems. JuzzyOnline includes features for generating figures for all types of fuzzy sets. It is available here.

Juzzy

Juzzy is a free, open-source, Java based library for the design and implementation of type-1, interval and general type-2 set and system based applications. It is available here

fuzzycreator

Fuzzycreator is a free, open-source, python based toolkit that facilitates the automatic generation of type-1 and type-2 fuzzy sets from data, and their analysis through measures such as similarity and distance. The toolkit and a guide on how to use it is available here.

SyFSeL (Synthetic Fuzzy Set Library)

SyFSeL is a free open-source library that automatically generates synthetic fuzzy sets aimed to for use in empirically testing methods developed for fuzzy sets. SyFSeL generates as many sets as desired, with specified membership function type (normal, bi-modal or multi-modal) and fuzzy set type (type-1 or type-2) to enable users to emulate real data. Fuzzy sets are stored in csv format so users can easily import the generated sets into their own fuzzy systems software and SyFSeL can also create graphical plots of the generated sets.


The library is available here.


For more information on the library and how to use it, see the related paper here.

DECSYS – Discrete and Ellipse-based response Capture SYStem

DECSYS is a newly developed open-source software tool, which enables the creation and administration of digital surveys that elicit both conventional and interval-valued responses. DECSYS incorporates a range of features, and is designed to maximise versatility for experimenters and usability for participants. Surveys can be conducted either locally or online, and results easily exported.


More information can be found here.

Fuzzyr

FuzzyR is a free, open-source fuzzy logic toolbox for the R programming language. Whilst keeping existing functionalities of the previous toolboxes (e.g. FuzzyToolkitUoN), the main extension of the FuzzyR toolbox is the capability to optimise 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.

​In the latest release we have made an extension of the toolbox for non-singleton fuzzy logic systems. The Source code for the experiments used in the paper 'An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems' is available here.

The toolbox is available on CRAN here

More information can be found in the paper here.

Juzzy Constrained

Juzzy Constrained is an open-source library, written in Java, that has been developed as an extension of the fuzzy library Juzzy. It adds the support for constrained interval type-2 (CIT2) fuzzy sets and systems and is available here.

Related papers:

[1] P. D'Alterio, J. M. Garibaldi, R. John and C. Wagner, "Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java", IEEE World Congress on Computational Intelligence (WCCI) 2020

[2] P. D'Alterio, J. M. Garibaldi, R. John and A. Pourabdollah, "Constrained Interval Type-2 Fuzzy Sets," in IEEE Transactions on Fuzzy Systems

JuzzyPy

JuzzyPy is an open-source, free, Python library allowing for the creation and evaluation of type-1, interval type-2, and general type-2 fuzzy logic systems. JuzzyPy also enables visualization of fuzzy sets and is available here.

MABLAR

MABLAR and MABLAR-CD are two frameworks which are designed to generte rules which capture different types of causal relationships between variables from a given data set. The original paper of MABLAR is available here, while the original paper of MABLAR-CD is available here. The source code of MABLAR frameworks is available the Github (MABLAR based on the Wang-Mendel algorithm and MABLAR based on the FURIA algorithm). We also provide some simulated data sets for testing the performance, which is available here. The details of these data sets is in our under review paper. We will update this page once the paper is accepted.

The python package of MABLAR frameworks now is available here. The package is actively being developed. Feedbacks are highly encouraged :)