FuzzyLogic.jl: a Flexible Library for Efficient and Productive Fuzzy Inference
This provides a new tool for researchers and practitioners in fuzzy logic and AI, but it is incremental as it builds on existing fuzzy inference methods.
The paper introduces FuzzyLogic.jl, a Julia library for fuzzy inference that is designed to be user-friendly, flexible, and efficient, achieving significant speedup compared to the Matlab fuzzy toolbox in benchmarks.
This paper introduces \textsc{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.