GENERATING FUZZY RULES FROM EXAMPLES USING GENETIC ALGORITHMS
GENERATING FUZZY RULES FROM EXAMPLES USING GENETIC ALGORITHMS
复制标题
使用遗传算法从示例生成模糊规则
DOI：
10.1142/9789812830753_0002
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发表时间：
1995
影响因子：
11.9
通讯作者：
J. Verdegay
中科院分区：
文献类型：
--
作者：
F. Herrera;M. Lozano;J. Verdegay
在模糊系统的开发中，理想模糊规则的生成问题是一个非常重要的问题。本文的目的是提出一种利用遗传算法从实例中学习的模糊控制规则生成方法。我们提出了一种真正的编码遗传算法来学习模糊规则，并提出了一个迭代过程来获得一组规则，该规则集覆盖了先前确定的覆盖值的示例集。
The problem of generation desirable fuzzy rules is very important in the development of fuzzy systems. The purpose of this paper is to present a generation method of fuzzy control rules by learning from examples using genetic algorithms. We propose a real coded genetic algorithm for learning fuzzy rules, and an iterative process for obtaining a set of rules which covers the examples set with a covering value previously deened.