lucaferranti/fuzzylogic.jl

Julia package for fuzzy inference

FuzzyLogic.jl

Info	Build status	Documentation	Contributing	Citation

A Julia library for fuzzy logic and applications.

If you use this in your research, please cite it as

@INPROCEEDINGS{ferranti2023fuzzylogicjl,
  author={Ferranti, Luca and Boutellier, Jani},
  booktitle={2023 IEEE International Conference on Fuzzy Systems (FUZZ)}, 
  title={FuzzyLogic.jl: A Flexible Library for Efficient and Productive Fuzzy Inference}, 
  year={2023},
  pages={1-5},
  doi={10.1109/FUZZ52849.2023.10309777}}

Features

• Rich! Mamdani and Sugeno inference systems, both Type-1 and Type-2, several membership functions and algoritms options available.
• Compatible! Read your models from IEC 61131-7 Fuzzy Control Language, IEEE 1855-2016 Fuzzy Markup Language and Matlab Fuzzy toolbox .fis files.
• Expressive! Clear Domain Specific Language to write your model as human readable Julia code
• Productive! Several visualization tools to help debug and tune your model.
• Portable! Compile your final model to Julia code.

Installation

1. If you haven't already, install Julia. The easiest way is to install Juliaup. This allows to easily manage julia versions.

2. Open the terminal and start a julia session by simply typing julia

3. Install the library by typing

using Pkg; Pkg.add("FuzzyLogic")

4. The package can now be loaded (in the interactive REPL or in a script file) with the command

using FuzzyLogic

5. That's it, have fun!

Quickstart example

fis = @mamfis function tipper(service, food)::tip
    service := begin
      domain = 0:10
      poor = GaussianMF(0.0, 1.5)
      good = GaussianMF(5.0, 1.5)
      excellent = GaussianMF(10.0, 1.5)
    end

    food := begin
      domain = 0:10
      rancid = TrapezoidalMF(-2, 0, 1, 3)
      delicious = TrapezoidalMF(7, 9, 10, 12)
    end

    tip := begin
      domain = 0:30
      cheap = TriangularMF(0, 5, 10)
      average = TriangularMF(10, 15, 20)
      generous = TriangularMF(20, 25, 30)
    end

    service == poor || food == rancid --> tip == cheap
    service == good --> tip == average
    service == excellent || food == delicious --> tip == generous
end

fis(service=1, food=2)

Documentation

• STABLE: Documentation of the latest release
• DEV: Documentation of the version on main

Contributing

Contributions are welcome! Here is a small decision tree with useful links.

• To chat withe the core dev(s), you can use the element chat. This is a good entry point for less structured queries.

• If you find a bug or want to request a feature, open an issue.

• There is a discussion section on GitHub. You can use the helpdesk for asking for help on how to use the software or the show and tell to share with the world your work using FuzzyLogic.jl.

• You are also encouraged to send pull requests (PRs). For small changes, it is ok to open a PR directly. For bigger changes, it is advisable to discuss it in an issue first. Before opening a PR, make sure to check the contributing guidelines.

Copyright

• Copyright (c) 2022 Luca Ferranti, released under MIT license.

fuzzylogic.jl's Issues

initial roadmap

Basic types

• Mamdami inference system
• Sugeno inference system #5
• Type-2 inference systems #18

DSL

• weighted rules #16
• vector notation to avoid repetitive patterns #17

Visualizations

• plotting single variables
• plotting inference system

parsing

• parse julia code
• read matlab .fis files #15
• fuzzy control language #13
• fuzzy markup language #20

Code generation

• generate stand-alone julia code #21
• generate stand-alone c code

Others

• fuzzy clustering #11

Documentation

• mamdami tutorial
• sugeno tutorial #5
• type-2 tutorial
• control systems example
• image processing application #6
• API / internals documentation #5
• contributing guideline polishing

Infra

• basic CI (run tests deploy docs)
• literate integration for documentation

Real- valued arguments desirable for membership functions

Would like membership functions to be defined on real- valued arguments. Presently only integer- valued arguments are possible. Thanks.

include graphs of the membership functions [enhancement]:

Feature description

In the future docs, include graphs of the membership functions, both separately and overlaid in related groups.
(for the separately graphed membership functions, perhaps an alongside copy with a lighter gray curve tracking the derivative of each membership function).

TagBot trigger issue

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