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模态叠加原理确立了结构响应和模态参数的关系,利用这一原理能编制简单而有效的模态参数计算程序。但经过对试验数据的处理结果表明,直接利用试验数据作为计算的初参数,通常在迭代运算中数值发散,得不出正确的结果。本文论述了初参数优选的原理和方法,即在保证总方差为最小的情况下进行初参数优选,解决了迭代运算中的数值发散问题。从而使模态叠加原理能够有效的用于模态参数识别,并可在一般微处理机上进行计算。
The modal superposition principle establishes the relationship between the structural response and the modal parameters. By using this principle, a simple and effective modal parameter calculation program can be compiled. However, after the experimental data processing results show that the direct use of experimental data as the initial parameters of the calculation, the numerical iteration usually divergence, can not get the correct results. This paper discusses the principle and method of optimizing the initial parameters, that is, initial parameters are optimized under the condition of ensuring the minimum total variance and the numerical divergence problem in iterative computation is solved. So that the modal superposition principle can be effectively used for modal parameter identification, and can be calculated on the general microprocessor.