Training ANFIS Using Artificial Bee Colony Algorithm

被引：15
|
作者：
Karaboga, Dervis [1 ]
Kaya, Ebubekir [2 ]
机构：
[1] Erciyes Univ, Dept Comp Engn, Kayseri, Turkey
[2] Nevsehir Univ, Dept Comp Sci, Nevsehir, Turkey
来源：
2013 IEEE INTERNATIONAL SYMPOSIUM ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (IEEE INISTA) | 2013年
关键词：
component; ANFIS; artificial bee colony; identification; neuro-fuzzy; swarm intelligent; PARTICLE SWARM OPTIMIZATION; SYSTEM;
D O I：
10.1109/INISTA.2013.6577625
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduces a new approach for training the adaptive network based fuzzy inference system (ANFIS). In this study, we apply one of the swarm intelligent branches, named artificial bee colony algorithm (ABC) for training. We use ABC for training the antecedent parameters and the conclusion parameters. The proposed method is applied to identification of the nonlinear system. The simulation results show that in comparison with genetic algorithm (GA), backpropagation (BP) and hybrid learning (HL) that is a combination of least-squares and backpropagation. The results show ABC optimizes ANFIS parameters are better than GA, BL and HL.
引用
收藏
页数：5
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