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1.
在充分利用部分输入已确知而部分输入未知的激励特性的基础上,提出了结构动力复合反演的分解算法,该算法从源头上消除了迭代过程中参数识别与荷载反演的相互影响,降低了问题的计算规模。对于线性参数系统,该算法不经过任何迭代计算即可一次性完成结构参数识别及荷载反演。将其与松弛法结合,可解决非线性参数系统的识别问题,与文献[4]的方法比较,其收敛速度有显著提高。  相似文献   

2.
部分输入未知条件下结构参数识别法研究   总被引:12,自引:0,他引:12  
深入研究了输入信息测试不完备条件下的结构参数识别问题,从理论上论证了部分输入未知时动力复合反演问题补偿算法的实质,指出了全量补偿算法在严格意义上的适用条件,提出了更为简便的二阶段识别法,使部分输入未知条件下结构参数识别的理论进一步完善,并可为工程应用提供指导。数值算例验证了理论上的正确性。  相似文献   

3.
一种结构参数识别的两阶段方法   总被引:16,自引:1,他引:15  
冯新  周晶 《计算力学学报》2002,19(2):222-227
针对测量信息不完备的剪切型结构 ,建立了一种两阶段系统识别的复合反演方法 ,这种方法包括两部分 :子结构地震动反演和结构参数识别。首先 ,选取可观测的子结构 ,利用一维地震动作用于结构的力学特性 ,将子结构动力方程的有限元列式进行变换 ,得到适合于最小二乘法的简单形式 ,解决了测量信息不完备及结构参数未知条件下的地震动反演问题。其次 ,根据子结构反演得到的地震动输入 ,采用结构参数时域识别技术中的加权整体迭代 -广义卡尔曼滤波器方法 ,成功地识别出了有限测量条件下单元水平结构参数  相似文献   

4.
高层建筑风荷载反演研究   总被引:2,自引:0,他引:2  
陈隽  李杰 《力学季刊》2001,22(1):72-77
本文研究了结构参数未知条件下的高层建筑风荷载反演问题,通过将平均风速的实测特性作为识别计算中的辅助条件,文中提出了一类荷载归一化统计平均方法,结合工程实例,进行了高层建筑风荷载反演分析,结果表明,本文建议方法可以在幅值、时程等方面均给出良好的风荷载反演结果,同时识别得到的结构参数具有良好的精度。从而为高层建筑风荷载研究提供了一条可行途径。  相似文献   

5.
部分输入未知时求解动力复合反演问题的补偿算法   总被引:17,自引:3,他引:14  
李杰  陈隽 《计算力学学报》2002,19(3):310-314
将作者提出的全量补偿算法[8] 推广为一类适用于一般多自由度系统的时域补偿识别算法。该方法是基于最小二乘原则的一类迭代计算方法 ,对于结构上的部分作用力已知的情况 ,可用来同时识别结构的参数并反演输入。文中应用不同类型的结构分析实例说明了此方法适用于实际工程动力检测的可能性。  相似文献   

6.
基于遗传算法的爆炸冲击荷载参数识别方法   总被引:5,自引:0,他引:5  
基于改进的遗传算法 ,建立了根据测试系统动力响应观测数据反演爆炸冲击荷载参数的数值方法。遗传算法为解决反问题的不适定性提供了强有力的手段。数值模拟结果表明 ,所提出的爆炸冲击荷载参数随机反演方法具有全局搜索能力 ,并且具有良好的抗观测噪音能力。当测试系统的观测相对误差为 10 %时 ,参数反演结果的误差小于 8% ,所建立的参数反演方法具有良好的鲁棒性。  相似文献   

7.
张晖  郅伦海 《实验力学》2023,(5):606-616
基于无迹卡尔曼滤波提出了一种高层建筑风荷载的反演算法,该方法利用有限测量楼层的风致响应数据,实时识别结构的未知风荷载和风致响应。通过典型高层建筑的风洞试验进行风荷载反演实例分析,验证了该方法的准确性和适用性,评估了模态参数误差、测量噪声水平对风荷载反演的影响。研究结果表明,文中提出的算法对模态参数误差不敏感,在一定噪声水平下反演的结果基本能够满足实际工程需要,该算法为实时评估高层建筑的风荷载和风致响应提供了有效的工具。  相似文献   

8.
通过一种时域自适应算法,建立了求解变速移动荷载下梁的多宗量反问题的数值模型,可同时识别移动荷载和梁的物性参数.正问题采用时域自适应算法和FEM建模,并可由此方便地推导敏度公式;在反问题求解中采用Levenberg-Marquardt法,计算表明该方法具有较好的抗不适定性.通过两个算例,对所提算法进行了数值验证,并探讨了噪声和测点的变化对反演结果的影响,结果令人满意.  相似文献   

9.
未知输入条件下的结构物理参数识别研究   总被引:26,自引:4,他引:22  
研究在输入信息未知条件下识别结构物理参数的问题,根据建筑结构风荷载的作用特点,提出一类时域识别算法,用于高层建筑结构的结构物理参数识别  相似文献   

10.
基于模态参数考虑边界条件变异的桥梁结构损伤识别   总被引:1,自引:0,他引:1  
施洲  赵人达 《应用力学学报》2012,29(2):191-196,241
根据桥梁结构的实际工程特性,分析其边界条件变异、结构损伤及其参数变化,采用约束优化理论,建立以实测和理论模态参数误差平方和最小为目标函数的优化反演问题。基于矩阵摄动理论引入与结构动力方程对应的特征值和特征向量的一阶、二阶摄动量,将优化反演问题简化为非线性最小二乘法优化反演问题。针对桥梁结构边界条件对模态参数影响显著的实际情况,实施桥梁结构边界条件预识别,采用单元模态应变能方法预定位损伤,提出考虑边界条件变异的桥梁结构损伤识别具体流程。以一磁浮轨道梁方案为例,采用数值模拟进行边界条件变异及损伤的识别验证,结果表明:该方法能够有效识别边界条件的变异及构件损伤,识别参数的相对误差最大为12.48%,具有较高的识别精度。  相似文献   

11.
Cui  Ting  Ding  Feng 《Nonlinear dynamics》2023,111(9):8477-8496

This paper investigates the parameter estimation issue for an input nonlinear multivariable state-space system. First, the canonical form of the input nonlinear multivariable state-space system is obtained through the linear transformation and the over-parameterization identification model of the considered system is derived. Second, by cutting down the redundant parameter estimates and extracting the unique parameter estimates from the parameter estimation vector in the least-squares identification method, we present an over-parameterization-based partially coupled average recursive extended least-squares parameter estimation algorithm to estimate the parameters. As for the unknown states in the parameter estimation algorithm, a new state estimator is designed to generate the state estimates. Third, in order to improve the computational efficiency of the parameter estimation algorithm, an over-parameterization-based multi-stage partially coupled average recursive extended least-squares algorithm is proposed. Finally, the computational efficiency analysis and the simulation examples are given to verify the effectiveness of the proposed algorithms.

  相似文献   

12.
For a Hammerstein input nonlinear system with a subspace state space linear element, this paper transforms the system into a bilinear identification model by using the property of the shift operator to the state space model and presents a recursive and an iterative least squares algorithms to generate parameter estimates and state estimates by using the hierarchical identification principle and by replacing the unknown state variables with their estimates. The proposed approaches are computationally more efficient than the over-parameterization model based least squares method.  相似文献   

13.
This paper develops a multistage least squares based iterative algorithm to estimate the parameters of feedback nonlinear systems with moving average noise from input–output data. Since that the identification model is bilinear on the unknown parameter space, the solution is to decompose a system into several subsystems with each of which is linear about its parameter vector, then to replace the unknown noise terms in the information vectors with their corresponding estimates at the previous iteration of each subsystem, and estimate each subsystem, respectively. The simulation results show that the proposed algorithm can work well.  相似文献   

14.
This paper discusses iterative identification problems for a class of output nonlinear systems (i.e., Wiener nonlinear systems) with moving average noises from input–output measurement data, based on the Newton iterative method. The basic idea is to decompose a nonlinear system into two subsystems, to replace the unknown variables in the information vectors with their corresponding estimates at the previous iteration, and to present a Newton iterative identification method using the hierarchical identification principle. The numerical simulation results indicate that the proposed algorithms are effective.  相似文献   

15.
This paper studies parameter identification problems for input nonlinear finite impulse response systems with moving average noise (i.e., input nonlinear finite impulse response moving average systems). Since the identification model of the system contains the product of the parameters of the nonlinear part and the linear part, we use the key variables separation technique and express the output of the system as the linear combination of all parameters, and then derive a Newton iterative identification method. The simulation results show that the proposed algorithm is effective.  相似文献   

16.
非线性系统参数辩识的一种频域模型   总被引:1,自引:0,他引:1  
本文基于对非线性系统的可分离性假设,将非线性弹性力和阻尼力分别分解为物理坐标下各点间相对位移和相对速度的幂级数函数,导出了一般多自由度非线性系统在恒幅激励下的广义频率响应函数与输入输出之间的迭代关系式,提出了非线性系统中基本线性部分的概念,进而了一种在实验条件下的系统物理参数辩识方法。  相似文献   

17.
We study stress-wave propagation in an impulsively forced split Hopkinson bar system incorporating a threaded interface. We first consider only primary transmission and reflection and reduce the problem to a first-order, strongly nonlinear ordinary differential equation governing the displacement across the interface, called the primary-pulse model. The interface is modeled as an adjusted-Iwan element, which is characterized by matching experimental and numerical eigenfrequencies as well as primary pulse amplitudes. We find that the adjusted-Iwan element parameters are dependent on preload and impact velocity (input force). A high-order finite element model paired with the identified adjusted-Iwan element is used to simulate multiple transmissions and reflections across the interface. We find that the finite element simulation reproduces the experimental results in both the wavelet and Fourier domains, validating the identification method. Our findings demonstrate that the primary-pulse model can be used for experimental parameter identification of nonlinear interfaces in waveguides.  相似文献   

18.
This paper focuses on the identification problem of Wiener nonlinear systems with non-uniform sampling. The mathematical model for the Wiener nonlinear system is established from the non-uniformly sampled input–output data. In order to solve the identification problem of the Wiener nonlinear system with the unmeasurable variables in the information vector, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. Finally, the simulation results indicate that the proposed algorithm is effective.  相似文献   

19.
This paper considers iterative identification problems for a Hammerstein nonlinear system which consists of a memoryless nonlinear block followed by a linear dynamical block. The difficulty of identification is that the Hammerstein nonlinear system contains the products of the parameters of the nonlinear part and the linear part, which leads to the unidentifiability of the parameters. In order to obtain unique parameter estimates, we express the output of the system as a linear combination of all the system parameters by means of the key-term separation principle and derive a gradient based iterative identification algorithm by replacing the unknown variables in the information vectors with their estimates. The simulation results indicate that the proposed algorithm can work well.  相似文献   

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