Parameter identification of Box-Jenkins systems based on the differential evolution algorithm

被引：0
|
作者：
Liu, Mengru [1 ]
Li, Junhong [1 ]
Zong, Tiancheng [1 ]
机构：
[1] Nantong Univ, Sch Elect Engn, Nantong 226019, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
parameter identification; system identification; differential evolution; Box-Jenkins systems; INSTRUMENTAL VARIABLE METHOD; ERRORS-IN-VARIABLES; MAXIMUM-LIKELIHOOD; LEAST-SQUARES; RECURSIVE-IDENTIFICATION; NEURAL-NETWORK; OPTIMIZATION; MODEL;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper considers the parameter estimation of the Box-Jenkins system. The differential evolution algorithm is used to identify the parameters of the Box-Jenkins system and it is compared to the improved particle swarm optimization algorithm. Simulation results show that these two algorithms can effectively identify the Box-Jenkins system. The differential evolution algorithm can give more accurate parameter estimation and identification accuracy than the improved particle swarm optimization algorithm.
引用
收藏
页码：1557 / 1561
页数：5
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