摘要
实际工程中,系统的输入一般是未知的或者是不可测量的,识别结构的模态参数只能采用响应信号。并且一般环境激励下结构的输入信号是可以假设为白噪声激励,其信号的功率谱可以视为一常数。笔者利用量子行为粒子群优化(Quantum-behaved Particle Swarm Optimization,QPSO)算法将环境激励下结构模态参数识别问题转化为一个多维优化问题。最后采用一数值模拟的三层框架对该方法进行验证。结果表明,量子粒子群算法可以有效地识别结构模态参数。该研究结果可作为结构损伤识别的基础。
In the actual projects, the input of a system is always unknown or immeasurable, so the modal parameters can only be estimated from the response data alone. Generally, the input signal of structures under ambient excitation can be assumed to be a white noise random signal, whose power spectrum is a constant. Using Quantum-behaved Particle Swarm Optimization（QPSO） algorithm in modal parameter estimation of civil structure under ambient excitation, the paper transfers the identification problem into a multi-dimension optimization problem. Finally, the structural modal parameter identification method based on Quantum-behaved Particle Swarm Optimization presented herein is verified by a numerical simulation of three-story frame structure. The calculations show that the method can effectively identify the structural modal parameters. The research results can be the basis for the structural damage identification.
出处
《苏州科技学院学报（工程技术版）》
CAS
2013年第4期11-15,共5页
Journal of Suzhou University of Science and Technology （Engineering and Technology）
基金
江苏省自然科学基金项目(BK2007549)
住房与城乡建设部研究开发项目(2008-K2-35)
苏州科技学院科研基金项目(XKZ201304)
苏州科技学院研究生科研创新计划项目(SKCX12S_025)
关键词
QPSO算法
互功率谱
环境激励
模态参数识别
QPSO
cross-power spectrum
ambient excitation
modal parameter identification
