权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Development of system identification algorithm for health monitoring of infrastructures

基础设施健康监测系统识别算法的开发

基本信息

批准号：
16560415
负责人：
NODA Shigeru
金额：
$ 2.37万
依托单位：
Kagawa University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2005
项目状态：
已结题

项目摘要

System identification technique to autoassociate hysteretic characteristics is proposed by learning a nonlinear dynamic structure which is represented by a system dynamics model. As this regard, neural network's learning serves as the means. The criterion function is defined by the sum of squared output errors which takes into account the energy of earthquake motion input to the structure. In order to obtain a regularly directed learning, effective use of the structural learning is considered as an alternative method for parameter and system identification problems. A global and local iteration procedure is proposed to obtain stable and fast convergency in the learning of recurrent networks.A method is developed to identify parameters on a hysteretic restoring system of non-degrading type by applying the wavelet transform. By this method, a nonlinear model of non-degrading type equivalent to any hysteretic system may be identified in terms of the model's parameters at the stage of their stable convergency to optimal ones. Numerical examples give satisfactory results to identify dynamic parameters of model structure with 3-degree-of-freedom.Effective identification scheme for hysteretic, degrading multi-degree of freedom shear beam structures is developed using importance sampling and rejection sampling filters. The effects and the accuracy of this procedure are analyzed by comparing with the conventional methods. Using converged parameters resimulated responses and hysteretic force characteristic are compared with known true ones. Stable solutions as well as their fast convergency to the optimum ones are obtained. It is found by numerical examples that the proposed method is a powerful tool for parameter identification.

通过学习由系统动力学模型表示的非线性动态结构，提出了自关联滞回特性的系统辨识技术。在这方面，神经网络的学习作为手段。准则函数定义为输出误差的平方和，它考虑了输入到结构的地震运动能量。为了获得有规则的有向学习，有效地利用结构学习作为参数和系统辨识问题的一种替代方法。为了在递归网络学习中获得稳定快速的收敛，提出了一种全局和局部迭代的方法，并利用小波变换对非退化类型的滞后恢复系统进行了参数辨识。用这种方法，可以根据模型稳定收敛到最优阶段的参数来辨识等价于任何滞后系统的非退化类型的非线性模型。数值算例对三自由度模型结构的动力参数进行了识别，获得了令人满意的结果。针对滞回、退化的多自由度剪力梁结构，提出了基于重要性采样和剔除采样滤波的有效识别方案。通过与常规方法的比较，分析了该方法的效果和精度。利用收敛后的参数，重新模拟了结构的响应和滞回力特性，并与已知的真实结果进行了比较。得到了稳定解及其快速收敛到最优解的结果。数值算例表明，该方法是一种强有力的参数辨识工具。