摘要
研究了输入荷载未知条件下的结构参数识别及荷载反演问题,该问题最终归结为一个非线性的优化问题求解,根据目标函数、约束条件的具体特性,采用BFGS算法作为局部搜索算子,构造了基于浮点编码的混合遗传算法。针对系统输入未知的激励特性,采用分解反演的计算策略,从而提高了动力反演中混合遗传算法的稳健性和收敛速度。数值算例表明,这种方法具有很好的参数识别精度及荷载反演效果,对测试噪声有较强的适应能力。
The structural parameter identification and the load identification of inverse analysis are studied. The resultant nonlinear global optimization problem is solved using a hybrid genetic algorithm in which the BFGS algorithm is chosen for local search. With no information of input excitation forces, a calculation strategy of decomposition inversion is presented, which improves the robustness and convergence rate of the hybrid genetic algorithm. Numerical simulation studies are performed. For verification purpose, both the noise-free and noise-included output responses are considered. Results show that the proposed method is efficient not only for structural parameter identification but also for load identification in inverse analysis.
出处
《工程力学》
EI
CSCD
北大核心
2005年第3期21-25,共5页
Engineering Mechanics
关键词
结构工程
动力反演
优化
遗传算法
分解反演
Computer simulation
Convergence of numerical methods
Dynamic response
Genetic algorithms
Inverse problems
Structural optimization
