System Identification Using Self-Adaptive Group Particle Swarm Optimization

被引：0
|
作者：
Lin, Chun-Hui [1 ]
Lee, Chin-Ling [2 ]
Lin, Cheng-Jian [3 ]
机构：
[1] Nation Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 701, Taiwan
[2] Natl Taichung Univ Sci & Technol, Dept Int Business, Taichung 404, Taiwan
[3] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung 411, Taiwan
来源：
2018 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2018) | 2018年
关键词：
Fuzzy cerebellar model articulation controller; entropy; Nelder-Mead; particle swarm optimization; prediction; identification;
D O I：
10.1109/IS3C.2018.00085
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This article presents an efficient TSK-type recurrent fuzzy cerebellar model articulation controller (T-RFCMAC) model based on dynamic-group-based hybrid evolutionary algorithm (DGHEA) for solving identification and prediction problems.
引用
收藏
页码：310 / 313
页数：4
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