Fuzzy regression: a genetic programming approach

被引：0
|
作者：
Feuring, T [1 ]
Golubski, W [1 ]
Gassmann, M [1 ]
机构：
[1] Univ Siegen, Dept Elect Engn & Comp Sci, D-57068 Siegen, Germany
来源：
KES'2000: FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, VOLS 1 AND 2, PROCEEDINGS | 2000年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Given some data pairs ((X) over bar (i), (Y) over bar (i)) 1 less than or equal to i less than or equal to k of fuzzy numbers we are interesting in finding a fuzzy function F with best fits the given data. Because of fuzzy arithmetic we cannot compute a fuzzy function with F((X) over bar (i)) = (Y) over bar (i) for all i as in the crisp case. Therefore we used a genetic programming approach for finding a suitable fuzzy function. We will present some tests and argue that this method is quite suitable for obtaining fuzzy function which can explain the given data.
引用
收藏
页码：349 / 352
页数：4
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