[1]雷宇翔,缑锦,王成,等.DE-ICA优化算法在工作模态参数识别的应用[J].华侨大学学报(自然科学版),2018,39(2):286-292.[doi:10.11830/ISSN.1000-5013.201606108]
 LEI Yuxiang,GOU Jin,WANG Cheng,et al.Application of DE-ICA Optimization Algorithm in Operating Modal of Parameter Identification[J].Journal of Huaqiao University(Natural Science),2018,39(2):286-292.[doi:10.11830/ISSN.1000-5013.201606108]
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DE-ICA优化算法在工作模态参数识别的应用()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第39卷
期数:
2018年第2期
页码:
286-292
栏目:
出版日期:
2018-03-20

文章信息/Info

Title:
Application of DE-ICA Optimization Algorithm in Operating Modal of Parameter Identification
文章编号:
1000-5013(2018)02-0286-07
作者:
雷宇翔 缑锦 王成 罗伟
华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
LEI Yuxiang GOU Jin WANG Cheng LUO Wei
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
参数识别 工作模态 独立成分分析 差分进化 随机寻优策略
Keywords:
parameter identification operating modal independent component analysis differential evolution stochastic optimization strategy
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201606108
文献标志码:
A
摘要:
提出一种差分进化(DE)改进的独立成分分析(ICA)优化算法,解决工作模态参数识别时容易陷入局部最优,难以识别出高阶模态参数的问题.通过对悬臂梁的ANSYS仿真数据对比可知:相对于传统的ICA方法,结合差分进化算法的ICA识别的模态参数精度更高,且能分离出更多的高阶模态,更适合于高阶模态参数的识别.
Abstract:
An independent solve the problem of the parameter identification in operating model, in which component analysis(ICA)algorithm modified by differential evolution(DE)is proposed. To process the operating modal analysis problem, which is easy to go into local optima and difficult to identify higher order modal parameters. ANSYS simulation result on cantilever shows that, comparing to the traditional ICA, the ICA combined with DE identified the modal parameter is more accurately, and is able to extract more higher-order modal, hence is more suitable for solving higher modal parameter identification.

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备注/Memo

备注/Memo:
收稿日期: 2016-06-02
通信作者: 缑锦(1978-),男,教授,博士,主要从事知识工程的研究.E-mail:goujin@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61572204, 51305142); 华侨大学研究生科研创新能力培育计划项目 (1511314029)
更新日期/Last Update: 2018-03-20