【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。更多还原

【Abstract】 In this paper, 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.更多还原