The FuzzyLite Libraries for Fuzzy Logic Control

Fuzzy Logic Controllers (FLCs) are mathematical models designed to control systems by means of fuzzy logic. Their simplicity, flexibility, interpretability, and handling of uncertainty have seen them applied to address different problems in a variety of domains. The seminal ideas of FLCs date back to 1965, and today there are more than 20 software libraries that provide such a functionality with different degrees of success. In spite of the widespread usage of FLCs, many of these libraries have not yet been thoroughly compared, hence raising questions about their correctness, performance, and accuracy when having to choose a library among them. In this article, we compare some of the most relevant libraries to design and operate FLCs, namely the FuzzyLite libraries, Matlab, Octave, and jFuzzyLogic. These libraries are evaluated on a set of 20 benchmarks that include Mamdani and Takagi-Sugeno FLCs as well as different membership functions. Our focus is on the performance and accuracy of the libraries, but we also consider the number of features and the amount of source code documentation to rate their overall quality. The results show that the FuzzyLite libraries offer the most accurate results, the highest number of features, the second best performance, and the second most documented source code, thus ranking them first for overall quality. The next libraries in the rankings are Octave, Matlab, and jFuzzyLogic (respectively). Our analysis of results finds explanations for the differences in performance and accuracy between the libraries, which provides useful information not only to further improve their quality, but also for users to make better and more informed decisions when having to choose one.