基于量子粒子群算法的结构模态参数识别

以由结构输入输出数据计算所得实测频响函数与理论频响函数差值最小化为优化目标,通过对理论频响函数中所含结构模态参数搜索取值使目标函数最小,即将结构模态参数识别问题转化为优化问题。采用量子粒子群算法对此过程优化计算,获得结构模态参数。用数值模拟六层框架结构对该方法进行验证。结果表明,量子粒子群可有效识别结构模态参数。     

基于R-TPBSS的结构模态参数识别方法

提出了一种基于R-TPBSS算法的结构模态参数识别方法。该方法通过对响应信号进行稳健性白化处理,提高了算法的抗噪性。该方法将模态坐标和模态振型分别视为独立源信号和混合矩阵,以模态坐标的时间预测性大于响应信号的时间预测性为前提构造目标函数,通过优化目标函数,直接从结构自由响应中分离出各个模态,配合单点模态参数识别方法,提取出结构的模态参数。仿真结果表明,此方法具有很高的识别精度,对噪声很好的鲁棒性,密集模态下,同样能够准确的识别出结构的模态参数。     

主动桁架模态的试验研究

以一个78杆空间桁架为试验模型，研究线性主动结构构型的试验问题。对构型主动结构及特性分析的试验技术做了实践。采用耦合模态空间的二次型最优控制算法，利用压电堆作动器，构型了主动桁架。采用dAPACE半实物物理仿真操作平台，分别测试了被动桁架和主动桁架的模态振型和模态参数，并测试了主动桁架在随机激励作用下的传递函数。结果表明，主动结构由于引入了控制系统而使得结构的阻尼增大，同时由于在压电作动器附近的杆单元的模态受到抑制，所以主动桁架的模态与相应的被动桁架的模态存在着较大的差别。     

量子粒子群结合小波变换识别结构模态参数

通过对结构响应进行连续小波变换将多自由度模态参数识别转化为多个单自由度模态参数识别。建立小波骨架理论公式与由结构输出信号计算而得的小波骨架之差为目标函数的优化问题,通过搜索包含于小波骨架理论公式中的模态参数的取值而使目标值最小,从而将优化问题转化为模态参数识别问题。量子粒子群算法是一种基于群体智能理论的优化算法。将量子粒子群算法应用到上述方法中一次性识别出结构的频率、阻尼和振型。最后采用数值模拟的简支梁对该方法进行有效性验证。结果表明,量子粒子群算法结合连续小波变换可以有效地识别环境激励下的结构模态参数。     

基于轴向模态应变能比法的三维桁架结构损伤定位方法

本文提出了可用于诊断三维桁架结构的损伤指标——轴向模态应变能比法。该指标仅利用结构的前两阶模态参数,无需转动自由度信息。通过四腿导管架海洋平台结构有限元模型,数值模拟不同构件的多种损伤工况对该指标进行验证,最后通过海洋平台物理模型实验对该方法进行了实验验证。验证结果表明本文提出的轴向模态应变能比法可有效地诊断三维桁架结构的损伤。     

铰接式空间桁架结构模态试验研究

铰接式空间桁架结构铰链多、柔性大,其地面环境下的动力学特性较难准确获取。桁架结构地面模态试验易受试验条件的影响。采用锤击法对桁架结构进行模态试验,获得了桁架结构的模态参数,并通过模态置信准则验证了试验结果的可靠性。结合不同试验工况,分析了悬挂条件和拾振点位置对桁架结构模态试验结果的影响。建立了桁架结构有限元模型并进行模态分析,与试验结果进行对比。试验结果表明:降低悬挂附加刚度有利于避免模态耦合;悬挂点均匀对称布置可减轻铰链滑移对结构整体刚度的削弱,提高试验的可重复性;拾振点居中布置有利于提高辨识模态的完备性;增加悬挂长度可降低悬挂效应对低阶模态的影响。研究结果对大型含铰柔性结构的地面模态试验具有借鉴意义。     

模糊聚类算法稳定图应用于桥梁结构参数识别

将模糊聚类算法应用于稳定图理论,并将该稳定图与协方差驱动的随机子空间法相结合,进行桥梁结构模态参数识别&;#61472;。提出不考虑阻尼的影响,以频率为横坐标,以模态保证准则MAC中任一列数据为纵坐标的模糊聚类算法稳定图。通过比较稳定图中各聚类圆大小的方法进行稳定图真假模态的判别,从而使模态判别不再需要人的主观参与而变得更加智能和准确。通过简支梁和连续梁仿真分析验证了模糊聚类算法稳定图用于桥梁结构参数识别的准确性和较强的抗干扰性。     

结构损伤检测的人工鱼群算法

结构损伤检测实际上属于系统识别的问题,其最终目标是识别结构损伤前后物理参数的变化。可利用实测结构模态参数建立方程求解得到结构物理参数,该过程在数学上往往转化为求解约束优化问题。由此,尝试采用人工鱼群算法来求解这类大型土木工程约束优化问题,首先介绍了算法的参数定义、行为描述及算法流程,然后利用经典测试函数对算法计算性能进行测试,最后给出了结构损伤识别这类约束优化问题的目标函数并通过数值仿真验证了该算法的有效性和鲁棒性。考虑测量噪声影响并通过不同损伤工况的数值仿真,研究结果表明,人工鱼群算法能有效地检测出损伤单元所处位置和损伤程度,因而将其应用到结构损伤检测领域是可行的。     

基于模糊聚类的模态参数自动识别

多参考点最小二乘复频域法（p-LSCF）是最近几年来提出的宽频带模态参数识别算法,该算法在抗干扰能力、识别密集模态、以及处理大阻尼复杂结构方面具有非常好的特性。区分真实极点与计算极点是该算法中非常重要的一步,这项工作通常是借助于稳定图由人工完成。对于复杂结构,这个过程尤其繁琐并且耗时。模糊聚类肥西已经在多个领域得到了成功的应用,通过将模糊聚类作为自动分析稳定图的工具引入到模态分析中,实现了模态参数的自动识别。     

GEP在桁架结构优化中的应用

介绍基因表达式编程(GEP)算法的基本原理和在参数优化中的实现过程,并将该算法应用于桁架结构的优化设计。针对标准GEP容易陷入局部最优解,且收敛速度慢的缺陷,对算法引入回溯机制,用停滞前一代的精英个体替换当前种群中所有适应度最差的个体,使较优个体有更多机会向不同方向进化,扩大最优解的搜索空间。25杆空间桁架的截面优化设计结果证明:算法改进后,搜索效率得到明显提高,并通过72杆空间桁架算例,证明了该方法在结构优化中的可行性和有效性。     

基于模式搜索算法的结构被动控制系统参数优化研究

提出减震结构被动控制系统优化分析数学模型,结合优化模型特点与主要优化算法的数学原理,选取模式搜索算法进行结构减震被动控制系统参数优化分析。通过编制目标函数程序,实现了Matlab模式搜索工具箱与Ansys减震模型的接口。多峰值函数的极值求解过程印证了模式搜索工具箱在解决该类优化问题时的有效性,而框架减震模型参数优化分析过程表明了优化程序的可行性以及优化结果的准确性,最终不同地震波下的优化分析结果证明了优化程序在减震结构参数优化中的稳定性。     

Finding stable attractors of a class of dissipative dynamical systems by numerical parameter switching

In this article we prove numerically, via computer graphic simulations and specific examples, that switching the control parameter of a dynamical system belonging to a class of dissipative continuous dynamical systems, one can obtain a stable attractor. In this purpose, while a fixed step-size numerical method approximates the solution of the mathematical model, the parameter control is switched every few integration steps, the switching scheme being time periodic. The switch occurs within a considered set of admissible parameter values. Moreover, we show via numerical experiments that the obtained synthesized attractor belongs to the class of all admissible attractors for the considered system and matches to the averaged attractor obtained with the control parameter replaced with the averaged switched parameter values. This switched strategy may force the system to evolve along on a stable attractor whatever the parameter values and introduces a convex structure inside of the attractor set via a bijection between the set of parameter control values and the attractors set. The algorithm besides its utility in systems stabilization, when some desired parameter control cannot be directly accessed, may serve as a model for the dynamics encountered in reality or in experiments, e.g. three species food chain models, electronic circuits, etc. This method, compared, for example, to the OGY algorithm where only small perturbations of parameter control can be issued, allows relatively large parameter perturbations. Also, it does not allow to stabilize an unstable orbit but, using an appropriate parameter switching algorithm, it allows to reach an already existing attractor. The present work extends the results we obtained previously and is applied to Lorenz, Rössler and Chen systems.     

Identification of Damage in Composite Structures: A Numerical Model

This article deals with a numerical technique for the identification of damage in composite structures. The laminated structure is modeled by finite elements. When the structure undergoes some kind of damage, its stiffness is reduced, changing the dynamic response. By considering the sensitivities of the orthogonality conditions of the mode shapes, a novel algorithm is formulated, which calculates a damage parameter in each finite element. The damage parameter is related directly to the reduction in stiffness of the damaged finite element. The proposed numerical model allows the identification of multiple damage and does not require a priori knowledge of the probable areas of damage. The algorithm is applied to a laminated rectangular plate and its efficiency is demonstrated through several damage simulations.     

Efficient algorithms for ordinary differential equation model identification of biological systems

Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.     

A novel algorithm for shape parameter selection in radial basis functions collocation method

Many Radial Basis Functions (RBFs) contain a free shape parameter that plays an important role for the application of Meshless method to the analysis of multilayered composite and sandwich plates. In most papers the authors end up choosing this shape parameter by trial and error or some other ad hoc means. In this paper a novel algorithm for shape parameter selection, based on a convergence analysis, is presented. The effectiveness of this algorithm is assessed by static analyses of laminated composite and sandwich plates.     

An integrated scheme for crystal plasticity analysis: Virtual grain structure generation

An integrated scheme is developed based on the controlled Poisson Voronoi tessellation (CPVT) model to generate the polycrystalline grain structure for micromechanics simulations. The proposed model of CPVT involves a single control parameter that is used to produce the grain structure with regularity control, by which the yielded tessellation varies from the purely random Voronoi tessellation to the regular hexagonal tessellation. The system extends the standard CPVT model by the addition of two features: a one-parameter gamma distribution and a mapping from a set of quantitative metallographic measurements to the distribution parameter. Based on this scheme, a grain structure can be constructed such that the virtual tessellation is statistically equivalent to the expected grain size distribution. To validate the modules that utilise the physical parameters that dictate the regularity, a series of theoretical investigations is performed. Efforts are devoted to proving the uniqueness of the mapping from the physical parameters to the distribution parameter and the regularity parameter. An efficient numerical algorithm is provided to facilitate the mapping solution process. A software system (VGRAIN) is developed implementing the proposed CPVT model to generate the grain structure for crystal plasticity finite element (CPFE) analysis. To demonstrate the proposed scheme and the VGRAIN system, a plane strain CPFE analysis is conducted. Two microstructures are generated with different regularities, and the deformation under uniaxial tension is simulated.     

改进遗传-狼群对节点序寻优的贝叶斯网络结构算法

贝叶斯网络是数据挖掘领域的一种重要方法。针对贝叶斯网络结构学习算法寻优效率低和易陷入局部最优的问题，提出一种基于改进的混合遗传-狼群对节点序寻优的贝叶斯网络结构学习算法。该算法首先利用深度优先搜索对最大支撑树的节点进行拓扑排序；然后利用动态变异及最优交叉算子构建适用于节点序寻优的改进捕食行为，引入动态参数因子来增强算法局部寻优能力；最后与K2算法结合得到最优的贝叶斯网络结构。用3种不同大小的标准网络数据集中进行实验，结果表明，该算法收敛到较优值，寻优效率高于其它同类优化算法。     

Dimensionality of color space in natural images

The color histogram (or color cloud) of a digital image displays the colors present in an image regardless of their spatial location and can be visualized in (R,G,B) coordinates. Therefore, it contains essential information about the structure of colors in natural scenes. The analysis and visual exploration of this structure is difficult. The color cloud being thick, its more dense points are hidden in the clutter. Thus, it is impossible to properly visualize the cloud density. This paper proposes a visualization method that also enables one to validate a general model for color clouds. It argues first by physical arguments that the color cloud must be essentially a two-dimensional (2D) manifold. A color cloud-filtering algorithm is proposed to reveal this 2D structure. A quantitative analysis shows that the reconstructed 2D manifold is strikingly close to the color cloud and only marginally depends on the filtering parameter. Thanks to this algorithm, it is finally possible to visualize the color cloud density as a gray-level function defined on the 2D manifold.     

A fast Bayesian approach using adaptive densifying approximation technique accelerated MCMC

A novel algorithm is presented in this study to improve the efficiency and accuracy of Bayesian approach for fast sampling of posterior distributions of the unknown structure parameters. This algorithm can save a computational cost by resolving the efficiency problem in Bayesian identifications. In this algorithm, an approximation model based on the radial basis function is first used to replace the actual joint posterior distribution of the unknown parameters. An adaptive densifying technique is then introduced to increase the accuracy of the approximation model by reconstructing them with densified samples. Finally, the marginal posterior distributions for each parameter with fine accuracy can be efficiently achieved using the Markov Chain Monte Carlo method based on the present densified approximation model. Two numerical examples are investigated to demonstrate that the present algorithm can achieve significant computational gains without sacrificing the accuracy.     

A Latent Class Model for Assessing Learning Structures

In the present paper, we discuss a latent structure analysis for assessing learning structures of acquiring two kinds of skill. This discussion presents a “pairwise” assessment procedure for explaining the learning structure of acquiring the skills concerned. We propose a latent class model for this objective. This model explains prerequisite and transfererence relations between the skills. A parameter estimation procedure is derived by use of the EM algorithm. A numerical example is also included to illustrate the estimation procedure.