FuzzyLogic.jl : A Flexible Library for Efficient and Productive Fuzzy Inference
Ferranti, Luca; Boutellier, Jani (2023-11-09)
Katso/
Tiedosto avautuu julkiseksi: : 09.11.2025
IEEE
09.11.2023
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2024091270905
https://urn.fi/URN:NBN:fi-fe2024091270905
Kuvaus
vertaisarvioitu
©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Tiivistelmä
This paper introduces FuzzyLOGIC.JL, a Julia library to perform fuzzy inference. The library is fully open-source and released under a permissive license. The core design principles of the library are: user-friendliness, flexibility, efficiency and interoperability. Particularly, our library is easy to use, allows to specify fuzzy systems in an expressive yet concise domain specific language, has several visualization tools, supports popular inference systems like Mamdani, Sugeno and Type-2 systems, can be easily expanded with custom user settings or algorithms and can perform fuzzy inference efficiently. It also allows reading fuzzy models from other formats such as Matlab. fis, FCL or FML. In this paper, we describe the library main features and benchmark it with a few examples, showing it achieves significant speedup compared to the Matlab fuzzy toolbox.
Kokoelmat
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