基于量子粒子群算法的结构模态参数识别  

Structural modal parameter identification based on quantum-behaved particle swarm optimization

作　　者：常军[1] 刘大山[1] 

机构地区：[1]苏州科技学院土木工程学院,江苏苏州215011

出　　处：《振动与冲击》2014年第14期72-76,88,共6页Journal of Vibration and Shock

基　　金：江苏省自然科学基金资助项目(BK2007549);建设部研究开发资助项目(2008-K2-35)

摘　　要：以由结构输入输出数据计算所得实测频响函数与理论频响函数差值最小化为优化目标,通过对理论频响函数中所含结构模态参数搜索取值使目标函数最小,即将结构模态参数识别问题转化为优化问题。采用量子粒子群算法对此过程优化计算,获得结构模态参数。用数值模拟六层框架结构对该方法进行验证。结果表明,量子粒子群可有效识别结构模态参数。Quantum-behaved particle swarm optimization,as a development of particle swarm optimization,is an optimization algorithm based on swarm intelligence.Thanks to its advantages of less parameters,simple programming and fast convergence,the quantum-behaved particle swarm optimization has received much concern.The difference between theoretical and calculated results of frequency response function was adopted as an objective function of optimization issue. The optimal objective value was gained through searching reasonable modal parameters.Then,the issue of structural modal identification was converted into an optimization issue.During the optimization procedure,the method of quantum-behaved particle swarm optimization was adopted and the modal parameters were identified.Finally,the modal parameter identification method based on quantum-behaved particle swarm optimization presented herein was verified through the numerical simulation of a six-story frame structure.The calculation results show that the method can effectively identify the structural modal parameters.

关 键 词：量子粒子群优化算法 粒子群优化算法 优化算法 频响函数 结构模态参数识别 

分 类 号：U441[建筑科学—桥梁与隧道工程]