FuzzyLogic.jl: productive fuzzy inference in Julia

This talk introduces FuzzyLogic.jl, a library for fuzzy inference, giving a tour of its features and design principles. Write your fuzzy model with an expressive Julia-like DSL or read your existing model from common formats, tune and explore with available algorithms and visualization tools, and generate efficient stand-alone Julia or C++ code for your final model. Finally, the talk will show how to use the library to solve engineering problems in control theory and image processing.

Since their introduction in the 60s, fuzzy logic inference methods have been successfully applied in several engineering domain, such as power electronics, control theory and signal processing.

This talk introduces FuzzyLogic.jl, a library for fuzzy logic and fuzzy inference, and gives a tour of its features and design principles.

User-friendliness: exploiting metaprogramming, it allows to implement fuzzy inference systems using expressive and concise Julia syntax. It also offers tools to visualize the inference system and different steps of the inference pipeline.

Interoperability: read fuzzy models from popular formats such as matlab .fis fromat, no need to manually rewrite or translate old legacy codes.

Flexibility: Easily(ish) tune the inference pipeline with your own algorithms.

Efficiency: after prototyping and exploring, generate efficient stand-alone julia or c++ code for your model.