Learning Fuzzy Systems by a Co-Evolutionary Artificial-Immune-Based Algorithm

被引：0
|
作者：
Vermaas, Luiz Lenarth G. [1 ]
Honorio, Leonardo M. [1 ]
Freire, Muriel [1 ]
Barbosa, Daniele [1 ]
机构：
[1] UNIFEI, Inst Technol & Elect Engn, Centro, MG, Brazil
来源：
FUZZY LOGIC AND APPLICATIONS | 2009年 / 5571卷
关键词：
Autonomous Vehicle; Co-Evolutionary Artificial Immune Systems; Fuzzy System Learning; EXTRACTION; RULES;
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：
To create a Fuzzy System from a numerical data, it is necessary to generate rules and memberships representing the analyzed set. This goal demands to break the problem into two parts: one responsible for learning the rules and another responsible for optimizing the memberships. This paper uses a Gradient-based Artificial Immune System with a different population for each of these parts. By simultaneously co-evolving these two populations, it is possible to exchange information between them enhancing the fitness of the final generated system. To demonstrate this approach, a fuzzy system for autonomous vehicle maneuvering was developed by observing a human driver.
引用
收藏
页码：312 / 319
页数：8
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