Improved Artificial Fish Swarm Algorithm and its Application in System Identification

被引：0
|
作者：
Zhu, Junlin [1 ]
Liu, Hui [1 ]
Wang, Zulin [1 ]
机构：
[1] Jiangxi Univ Sci & Technol, Coll Elect Engn & Automat, Ganzhou, Jiangxi, Peoples R China
来源：
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC & MECHANICAL ENGINEERING AND INFORMATION TECHNOLOGY (EMEIT-2012) | 2012年 / 23卷
关键词：
Artificial Fish Swarm Algorithm; Needle-in-haystack Problem; Parameter Identification;
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 order to solve the traditional identification method limitations, This paper presents an improved artificial fish swarm algorithm, Through the experiment of a typical Needle-in-haystack problem, Show that the improved artificial fish swarm algorithm has better ability of global optimization, faster convergence speed, higher accuracy of optimization. This algorithm is applied to the system parameter identification, Through to the linear system and nonlinear system parameter identification simulation, Results show that the algorithm has fast convergence, high accuracy advantages, Has important application value in Engineering.
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页数：4
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