动态最小二乘支持向量机学习算法     

邓兴升  花向红 《武汉大学学报(信息科学版)》2008,33(11):1122-1125

针对大样本集的训练问题和动态训练样本的模型更新问题,提出了动态最小二乘支持向量机学习算法.该算法充分利用已建好的模型,逐渐加入新样本,并可删除位于任何位置的非支持向量,避免了矩阵求逆运算,保证了算法的高效率.大坝变形及电离层延迟两个时间序列的预报实例表明,该算法具有计算时间短、预报精度高的特点.  相似文献   

基于改进PSO-SVM的噪声稳健建筑物变形监测方法     

张建奇 《北京测绘》2021,35(2):166-171

对高层、超高层建筑物进行实时,高精度的变形监测对提前预防安全隐患,保证人民生命财产安全具有重要意义。建筑物变形作为一种典型的随机性和微弱性过程,噪声等误差的存在会影响从中提取有用的变形信息。针对该问题,提出一种改进粒子群(Particle Swarm Optimization,PSO)算法优化支持向量机(Support Vector Machine,SVM)的噪声稳健建筑物变形监测方法,利用改进PSO算法的全局搜索能力对SVM的核参数进行优化,提升预测精度的同时增强算法的噪声稳健性。基于实测数据的试验结果表明,相对于传统交叉验证SVM和小波方法,所提方法可以获得更高的变形预测精度,并且在低信噪比条件下优势更加明显。  相似文献   

一种基于核学习的储集层渗透率预测新方法   总被引：2，自引：1，他引：2  

杨斌  匡立春  施泽进  孙中春 《物探化探计算技术》2005,27(2):119-123

基于核学习的支持向量机,是一种采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法,具有完备的理论基础。这里提出了核学习技术在储集层非均质特性描述中渗透率参数预测的新用途。在复杂地层中,基于支持向量机的智能和自适应模式识别能力而建立了常规测井多参数信息输入的渗透率预测模型,然后对实际油田储集层渗透率进行了预测。与常规线性回归模型预测结果相对比,所提出的方法更易于使用,很少受不确定因素的影响,并具有较强的信息整合能力以及更高的预测准确性和可信度。  相似文献   

万家寨引黄工程泥灰岩段隧洞岩石掘进机(TBM)卡机事故工程地质分析和事故处理   总被引：8，自引：1，他引：8  

尚彦军  王思敬  薛继洪  曲永新 《工程地质学报》2002,10(3):293-298

隧道全断面岩石掘进机 (TBM )的施工对地质条件适应性是一个不断认识和深化的过程 ,其中很多认识是来自已发生的各种各样事故和对事故原因的理性认识。本文是在对某隧洞TBM卡机地段的岩性 (包括工程性质 )、构造、地应力及其在TBM施工条件下的变化等方面进行工程地质力学研究的基础上 ,指出层间剪切带的存在及由此而产生的较大变形速率是导致事故发生的工程地质原因 ,并提出了相应的处理意见和建议。TBM卡机问题的顺利解决和它重新开始快速掘进证明了作者分析和工程处理措施的合理性。本文的分析有助于加深TBM施工对复杂地质条件适应性的认识 ,并可望为避免类似事故的再次发生提供参考和借鉴。  相似文献   

对目前岩石“全曲线”测试的若干看法   总被引：1，自引：0，他引：1  

马世民  马宇博 《岩土力学》2003,(Z1)

从电液伺服岩石压力试验机工作机理的角度,对目前岩石应力-应变全过程曲线(以下简称“全曲线”)测试中的一些问题进行了分析,提出了自己的看法,目的是为了解决“全曲线”测试的2个问题,即如何测出“全曲线”;如何使测出的“全曲线”真实反映岩石的力学特性,并将其用到工程中。  相似文献   

一种新型自动开槽机及其控制     

靳建刚  王丽红 《中国煤田地质》2004,16(5):60-62

针对棒、管料开槽的问题，提出了一种新型全自动开槽机。介绍了该开槽机的结构和工作原理，建立全自动开槽机相适应的液压系统，搭建了开槽机继电器——控制器控制电路，实现了该设备的全自动过程控制。  相似文献   

The Effect of Machine Behaviour and Mechanical Properties of Intact Sandstone Under Static and Dynamic Uniaxial Cyclic Loading   总被引：5，自引：0，他引：5  

M. N. Bagde  V. Petroš 《Rock Mechanics and Rock Engineering》2005,38(1):59-67

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Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China     

Mingjuan Xie  Geping Luo  Olaf Hellwich  Amaury Frankl  Wenqiang Zhang  Chunbo Chen  Chen Zhang  Philippe De Maeyer 《水文研究》2021,35(12):e14444

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.  相似文献   

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)     

Pijush Samui  T. G. Sitharam 《国际地质力学数值与分析法杂志》2010,34(7):755-770

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

高分一号遥感影像地质灾害信息提取方法研究   总被引：1，自引：0，他引：1  

李启源  王明常  王凤艳  谭洋  卢立吉 《测绘与空间地理信息》2016,(2):17-20

山区地质灾害发生频繁,受灾范围大,危害严重。提取地质灾害信息、估算受灾情况,对救灾工作极为重要。传统的遥感人工解译方法速度慢、效率低;计算机自动解译速度快但解译精度受影像质量影响大,大范围地区很难建立起普适性的解译模型。本文利用洮南市西部高分一号遥感数据,结合DEM生成三维影像,建立解译标志,解译基础地质信息,分析地质灾害成因;根据研究区泥石流地质灾害的主要影像特征,提取影像的分类属性,基于面向对象的分类方法,建立信息提取模型,快速提取出地质灾害敏感区域;再进行人机交互,精确提取出地质灾害的类型和范围,通过野外验证,该方法十分可靠,为大范围地质灾害信息快速提取和灾后救援提供科学依据。  相似文献