The FuzzyLite Libraries for Fuzzy Logic Control
The FuzzyLite Libraries for Fuzzy Logic Control
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用于模糊逻辑控制的 FuzzyLite 库
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发表时间：
2018
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通讯作者：
Juan Rada
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作者：
Juan Rada
模糊逻辑控制器（FLC）是通过模糊逻辑设计用于控制系统的数学模型。它们的简单性、灵活性、可解释性和对不确定性的处理使它们被应用于解决各种领域的不同问题。FLC的开创性思想可以追溯到1965年，今天有20多个软件库提供了这样的功能，并取得了不同程度的成功。尽管FLC被广泛使用，但其中许多库还没有被彻底比较，因此在必须从中选择库时，对它们的正确性，性能和准确性提出了问题。在本文中，我们比较了一些与设计和操作FLC最相关的库，即FuzzyLite库、Matlab、Octave和jFuzzyLogic。这些库在一组20个基准上进行评估，其中包括Mamdani和Takagi-Sugeno FLC以及不同的隶属函数。我们的重点是库的性能和准确性，但我们也考虑功能的数量和源代码文档的数量来评估其整体质量。结果显示，FuzzyLite库提供了最准确的结果，最多的功能，第二好的性能，第二多的文档源代码，因此在整体质量方面排名第一。排名中的下一个库分别是Octave、Matlab和jFuzzyLogic。我们对结果的分析找到了图书馆之间性能和准确性差异的解释，这不仅为进一步提高质量提供了有用的信息，而且还为用户在选择一个时做出更好，更明智的决定提供了有用的信息。
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.