Following problems are mainly discussed in this work : how to build a parametrical finite element ( fe ) model how to choose the design parameters , constraints and objective of the correction model how to evaluate different sensitivity calculating methods and choose optimization algorithms how to get the normal mode - shapes and describe the relativity between normal mode - shapes how to solve the correction problem of fe model by using structure optimization techniques in each paragraph , illustrative examples are used to explain the methods and finally an engineering application of an aircraft correction is presented in the end 主要进行了如下几个方面的工作：参数化有限元模型的建立；修正模型设计参数、约束及目标函数的确定；灵敏度方法的比较及优化算法的选取；固有振型的获取及模态相关性的定量描述；采用结构优化技术解决有限元模型修正问题。在模态修正过程中，由于修正固有振型涉及大量的有限元计算结果数据，此前，虽有不少文献在理论上研究了它的修正方法，但都没有给出工程应用实例，而本文解决了固有振型修正的工程应用问题。
The dual standard quantity ( the work piece and the discrete standard quantity ) mutual measuring and model verification methods are also proposed , which perfects the whole modifying process from data measuring , error separation , model establishment to real correction . after researching the discrete standard quantity system dynamic error separation technique , two error correction methods based on genetic algorithm and neural network mixed modeling technique are established . the two methods are the discrete standard quantity dynamic error direct / synchronous correction and prediction model correction ; the model ' s parameters and model ' s exercising method are also confirmed 设计了双标准量值(工件和离散标准量)互比测量的模型验证方法，完善了从数据测量、误差分离、模型建立到实际修正的整个修正过程；研究了离散标准量系统动态误差分离技术，建立了基于遗传进化算法与神经网络混合建模技术的两种误差修正方法? ?离散标准量动态误差直接(同步)修正方法和预报模型修正方法，并确定了模型结构参数和模型训练方法；分析了预报模型的多次预报性质，并得出了多次预报与多步预报的等效关系，确定了测量系统的有效预报范围以及模型参数对泛化误差的影响；进行了模型的对比实验验证和被测工件动态误差修正试验，成功地实现了任意二面角和圆分度的实时误差修正。
