Parameter identification of dynamical systems based on improved particle swarm optimization

被引：0
|
作者：
Ye, Meiying [1 ]
机构：
[1] Zhejiang Normal Univ, Coll Math & Phys, Jinhua 321004, Peoples R China
来源：
INTELLIGENT CONTROL AND AUTOMATION | 2006年 / 344卷
关键词：
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：
Improved Particle Swarm Optimization (IPSO), which is a new robust stochastic evolutionary computation algorithm based on the movement and intelligence of swarms, is proposed to estimate parameters of nonlinear dynamical systems. The effectiveness of the IPSO algorithms is compared with Genetic Algorithms (GAs) and standard Particle Swarm Optimization (PSO). Simulation results of two kinds of nonlinear dynamical systems will be illustrated to show that the more accurate estimations can be achieved by using the IPSO method.
引用
收藏
页码：351 / 360
页数：10
相关论文
共 50 条
  • [21] Parameter Extraction of Memristor Model Based on Improved Particle Swarm Optimization
    Wang, Lei
    Wu, Youyu
    PROCEEDINGS OF 2024 INTERNATIONAL CONFERENCE ON POWER ELECTRONICS AND ARTIFICIAL INTELLIGENCE, PEAI 2024, 2024, : 515 - 518
  • [22] Identification of Dynamical Systems Using a Broad Neural Network and Particle Swarm Optimization
    Han, Ran
    Wang, Rongjie
    Zeng, Guangmiao
    IEEE ACCESS, 2020, 8 : 132592 - 132602
  • [23] Multi-parameter identification of permanent magnet synchronous motor based on improved particle swarm optimization
    Liu X.-P.
    Hu W.-P.
    Zou Y.-L.
    Zhang Y.
    Dianji yu Kongzhi Xuebao/Electric Machines and Control, 2020, 24 (07): : 112 - 120
  • [24] Parameter identification of LuGre friction model for servo system based on improved particle swarm optimization algorithm
    Zhang Wenjing
    PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 3, 2007, : 135 - 139
  • [25] Parameter Identification of MR Damper Model Based on Particle Swarm Optimization
    Yang, Yonggang
    Ding, Youchuang
    Zhu, Shixing
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC2019), 2020, 582 : 555 - 563
  • [26] Parameter Identification of Train basic resistance Based on Particle Swarm Optimization
    Li Tianxiang
    Yang Hang
    Wang Chuanru
    Wang Qingyuan
    Sun Pengfei
    Feng Xiaoyun
    2022 IEEE 17TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2022, : 1572 - 1577
  • [27] Generator parameter identification based on extended particle swarm optimization method
    Hu, Jiasheng
    Guo, Chuangxin
    Cao, Yijia
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2004, 28 (06): : 35 - 40
  • [28] On convergence and parameter selection of an improved particle swarm optimization
    Chen, Xin
    Li, Yangmin
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2008, 6 (04) : 559 - 570
  • [29] Improved particle swarm algorithm for hydrological parameter optimization
    Jiang, Yan
    Liu, Changmin
    Huang, Chongchao
    Wu, Xianing
    APPLIED MATHEMATICS AND COMPUTATION, 2010, 217 (07) : 3207 - 3215
  • [30] Parameter Optimization of PID Controller Based on an Improved Particle Swarm Optimization for the Induction Motor
    Shi, Xia-bo
    Lin, Wei-xing
    MECHANICAL AND ELECTRONICS ENGINEERING III, PTS 1-5, 2012, 130-134 : 1938 - 1942
12345