共查询到18条相似文献,搜索用时 108 毫秒
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介绍了支持向量机的分类算法,构造了基于模态柔度相对变化量的损伤识别指标,并将此指标作为支持向量机的特征参数进行训练和结构的损伤识别,通过对简支梁仿真计算及试验结果表明:支持向量机对结构损伤的识别有着良好的抗噪性能,该方法对梁的损伤位置有较高的识别能力。 相似文献
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结构损伤识别可以归结为结构损伤参数的模式识别问题.对结构响应信号进行小波包分解可以获得各频带的信号能量,将此特征向量作为输入,利用支持向量机强大的模式分类功能,可以实现结构的损伤识别.在环境振动下,对1/10比例的单层网壳模型进行损伤识别试验,将不同的杆件沿径向进行相应程度的截面切割用以模拟不同程度的损伤状态.对不同损伤情况的加速度样本进行三层小波包分解,以相应频带的信号能量作为输入建立支持向量机,利用支持向量机对未训练样本的信号能量进行损伤分类.试验结果表明该方法简便准确,验证了小波包和支持向量机方法用于损失识别的有效性. 相似文献
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在增量式最小二乘支持向量机(SILS-SVM)方法的基础上,提出了加权特征向量最小二乘支持向量机(WEVLS-SVM)在线结构损伤识别方法。该方法根据训练数据贡献量的大小对数据进行加权,从而更适合于对结构的时变参数进行在线识别,同时较增量式算法有更小的累积误差。以一剪切型结构为例进行了数值模拟,分析结果表明,该方法与非加权的SILS-SVM方法相比,能更好地适应系统参数的变化,从而能很好地识别结构的损伤及其程度。 相似文献
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支持向量机是一种基于统计学习理论的机器学习方法,具有很好的回归和预测性能.本文提出了一种基于支持向量机回归的结构损伤识别方法,采用柔度作为支持向量机的输入向量,并以两跨连续梁为例进行仿真计算.结果表明,本文方法可以较好的从单点损伤情况预测出两点损伤情况的损伤位置和损伤程度. 相似文献
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在增量式最小二乘支持向量机(SILS—SVM)方法的基础上,提出了加权特征向量最小二乘支持向量机(WEVL8-SVM)在线结构损伤识别方法。该方法根据训练数据贡献量的大小对数据进行加权,从而更适合于对结构的时变参数进行在线识别,同时较增量式算法有更小的累积误差。以一剪切型结构为例进行了数值模拟,分析结果表明,该方法与非加权的SILS-SVM方法相比,能更好地适应系统参数的变化,从而能很好地识别结构的损伤及其程度。 相似文献
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采用矩阵摄动有限元方法,给出了结构损伤识别方程.为减小模型误差和测试误差对损伤识别结果的影响,利用残余力向量对损伤单元的敏感性,提出了广义残余力向量差的概念.为提高损伤识别结果精度和计算效率,联合采用广义残余力向量差定位与频率变化定量的结构损伤识别方法,先运用广义残余力向量差来初步确定损伤位置,再根据频率摄动运用测试精度较高的频率筛选计算损伤程度并确认损伤位置.实例证明,即使考虑测试误差,该方法仍然可以实现对损伤位置和程度的快速准确识别,具有较强的噪声鲁棒性. 相似文献
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将支持向量机方法用于框架结构的损伤检测,并通过一个算例对该方法进行了损伤识别效果的测试,试验表明支持向量机适用于单损伤的检测,对于损伤位置的判断,有很好的精确性,而且对损伤程度的预测也有很好的精度。 相似文献
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只要将内含子识别出来就可以得到准确的剪接位点。所以在此使用识别内含子的方法来获得剪接位点。首先利用支持向量机算法,分析供体位点和受体位点之间的内含子两端序列对于基因剪接的影响,并对内含子两端序列的长度与剪接的关联性进行了深入的研究。研究发现,内含子区别于伪内含子的特征信息同时存在于内含子两端大约各70个碱基。由于标准支持向量机受类别差异影响和噪声、野值数据干扰较重,使得分类能力不高,所以提出将一种改进的支持向量机算法……加权近似支持向量机应用于剪接位点的识别中,结果表明加权近似支持向量机在识别内含子的准确率方面要优于标准支持向量机。 相似文献
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矿井涌水水源识别的MMH支持向量机模型 总被引:4,自引:1,他引:3
提出一种新的多水源判别的H支持向量机模型。推导H支持向量机的理论推广误差公式,发现确保高优先级节点的推广性能是提高H支持向量机性能的有效途径;设计基于SVM最大间隔逐层分类、最小间隔逐层聚类构造H支持向量机的新方法,以各支持向量机节点的分类间隔为分类、聚类指标,通过TopDown,BottomUp两种方式混合构造H支持向量机,即MMH支持向量机。实验效果表明,MMH支持向量机结构简单、泛化能力强,不仅能正确区分各类水源,而且其层次结构能很好地反映各水源的层次关系。判别函数的法向量还可以指示各含水层水质化验指标的权重,为矿井涌水水源识别提供了新的科学方法。 相似文献
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Vibration characteristics of vaulted masonry monuments undergoing differential support settlement 总被引:1,自引:0,他引:1
This paper assesses the feasibility of vibration testing to detect structural damage caused by the settlement of buttresses in the Beverley Minster, a Gothic church located in the UK. Over the past eight centuries, the accumulated support settlements of the buttresses of Beverley Minster have pulled the main nave walls outward, causing severe separation along the edges of the masonry vaults. Bays closer to the main crossing tower have remained intact; however, at the west end of the Minster, the crack width between the walls and vaults has reached about 150 mm, leading to approximately 200 mm of sag at the crown of the vaults. Due to uneven settlement of buttresses along the nave of the church, the Minster now has ten nominally identical vaults at different damage states. In this work, two of these vaults representing the two extremes, the most damaged and undamaged structural states, are subjected to vibration testing with impact hammer excitation. From these vibration measurements, damage indicators are extracted in the modal, frequency, and time domains. In the modal domain, the differences between modal parameters are observed to be comparable to measurement uncertainty and hence insufficient to reach conclusions about the presence of vault damage. However, the amplitudes of frequency response functions in the frequency domain are observed to indicate a clear difference between the damaged and undamaged states of the structure. A time domain autoregressive model, support vector machine regression, is also found to be successful at indicating the differences between the two structural states of the vaults. We conclude that vibration measurements offer a practical solution to detect wall-vault separation in historic masonry monuments, provided that multiple damage indicators are evaluated. 相似文献
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针对振型是判断建筑物动力特性的主要参数,采用振型平方差法对结构的损伤进行定位,并就结构损伤对频率及振型的影响进行分析,找出结构损伤与动力特性参数间的关系,从而为判断结构损伤位置提供了参考. 相似文献
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Hamed FATHNEJAT Behrouz AHMADI-NEDUSHAN 《Frontiers of Structural and Civil Engineering》2020,14(4):907
In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation (RMSD) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model. 相似文献
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位移反分析的进化支持向量机研究 总被引:25,自引:8,他引:25
将支持向量机与遗传算法相结合,提出了一种用于位移反分析的进化支持向量机方法。这种方法基于试验设计和有限元计算获得学习样本和检验样本,用遗传算法搜索最优的支持向量机参数,用获得的最优模型进行学习,从而获得岩体的力学参数与位移之间的非线性映射关系,再用遗传算法从全局空间上搜索,进行岩体力学参数的识别。给出的两个算例结果是令人满意的。 相似文献