Neuro-fuzzy modeling of complex systems using genetic algorithms

被引：0
|
作者：
Farag, WA
Quintana, VH
LambertTorres, G
机构：
来源：
1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4 | 1997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：
In this paper, a genetic-based neuro-fuzzy (NF) approach is proposed to build and optimize fuzzy models. The learning algorithm of the Fuzzy-Neural Network (FNN) is divided into three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed to find the linguistic fuzzy rules. In the third phase, a new technique is used to apply a Genetic Algorithm (GA) to tune the membership functions of the fuzzy model optimally. A well-known example is used to investigate the performance of the proposed modeling approach, and compare it with the other modeling approaches.
引用
收藏
页码：444 / 449
页数：6
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