[1]常 军,刘大山.环境激励下结构模态参数识别的量子粒子群算法[J].西安建筑科技大学学报:自然科学版,2014,46(04):508-512.[doi:10.15986/j.1006-7930.2014.04.009]
 CHANG Jun,LIU Dashan.Quantum-behaved particles swarm optimization for structural modal parameters identification under ambient excitation[J].J.Xi’an Univ. of Arch. & Tech.:Natural Science Edition,2014,46(04):508-512.[doi:10.15986/j.1006-7930.2014.04.009]
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环境激励下结构模态参数识别的量子粒子群算法()
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西安建筑科技大学学报:自然科学版[ISSN:1006-7930/CN:61-1295/TU]

卷:
46
期数:
2014年04期
页码:
508-512
栏目:
出版日期:
2014-08-31

文章信息/Info

Title:
Quantum-behaved particles swarm optimization for structural modal parameters identification under ambient excitation
文章编号:
1006-7930(2014)04-0508-05
作者:
常 军刘大山
(苏州科技学院土木工程学院,江苏 苏州 215011)
Author(s):
CHANG Jun LIU Dashan
(School of Civil Engineering, University of Science and Technology of Suzhou, Suzhou 215011,China)
关键词:
量子粒子群优化算法环境激励互功率谱结构模态参数识别
Keywords:
quantum-behaved particle swarm optimization ambient excitation cross power spectrum structural modal parameters identification
分类号:
U441
DOI:
10.15986/j.1006-7930.2014.04.009
文献标志码:
A
摘要:
环境激励下的结构模态参数可以通过不同点输出信号的互功率谱识别出来.将包含结构模态参数的互功率谱理论公式 与不同点输出信号计算得到的互功率谱之差作为目标函数,通过搜索模态参数的取值而使目标函数最小,从而将优化问题转化为模态参数识别问题.量子粒子群算法是一种基于群体智能理论的优化算法.论文将量子粒子群算法应用到上述优化问题中识别环境激励下的结构模态参数.最后采用数值模拟的简支梁对该方法进行有效性验证.结果表明,量子粒子群可以有效地识别环境激励下的结构模态参数.
Abstract:
Modal parameters of structure under ambient excitation can be identified by cross power spectrum calculated from structural outputs of different parts. The difference between theoretical formula of cross power spectrum, including structural modal parameters to be identified, and the cross power spectrum calculated from structural output-only data, will be adopted as an objection function of optimization issue. The optimal objective value can be gained through searching reasonable modal parameters and Quantum-behaved Particle Swarm Optimization as a swarm intelligence optimization algorithm, will be used in the optimization issue above to identify the structural modal parameters under the ambient excitation. Finally, the modal parameters identification method based on Quantum-behaved Particle Swarm Optimization presented herein is verified by a numerical simulation of a simple-supported beam. The results show that Quantum-behaved Particle Swarm Optimization can effectively identify the structural modal parameters under ambient excitation.

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备注/Memo

备注/Memo:
收稿日期:2013-12-27 修改稿日期:2014-07-23
基金项目:国家科技支撑计划课题项目(2012BAJ11B01);江苏省自然科学基金项目(BK20141180);苏州科技学院科研基金项目(XKZ201304)
作者简介:常军(1973-),男,博士,副教授,主要从事结构健康监测方面的研究.E-mail: changjun21@126.com
更新日期/Last Update: 2015-10-06