共查询到20条相似文献，搜索用时 125 毫秒
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基于PSO和LSSVM的边坡稳定性评价方法 总被引：5，自引：0，他引：5
提出了基于粒子群算法（PSO）和最小二乘支持向量机（LSSVM）的边坡稳定性评价方法。该模型既利用了最小二乘支持向量机求解速度快、易于描述非线性关系的优良特性,同时也利用了粒子群算法快速全局优化的特点。粒子群算法用于搜索最小二乘支持向量机模型的最优参数,然后将模型用于预测边坡的安全系数。计算结果表明,该方法是合理的、有效的。 相似文献
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PSO-LSSVM模型在位移反分析中的应用 总被引：4，自引：1，他引：3
提出了一种基于均匀设计原理、最小二乘支持向量机(LSSVM)和粒子群优化算法(PSO)的快速位移反分析方法。该方法利用均匀设计和有限差分法获得学习样本,再用粒子群算法搜索最优的最小二乘支持向量机模型参数。并用最小二乘支持向量机回归模型建立反演参数与监测点位移值之间的非线性映射关系,最后用粒子群算法从全局空间上搜索与实测位移最吻合的反演参数。该反演模型利用了粒子群算法高效简单、均匀设计构造高质量小样本以及最小二乘支持向量机的小样本、泛化性能好的特点。将该模型应用于龙滩水电站左岸地下厂房区岩体地应力场的反演分析中,计算结果与实测的位移值和地应力值均吻合较好,说明了该模型在岩土工程快速反演分析中具有良好的应用价值。 相似文献
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针对黏介质成像存在的问题及最小二乘逆时偏移方法的优势,通过广义标准线性固体(GSLS)的三维黏声波动方程,基于三维黏声波动方程的伴随算子及最小二乘逆时偏移框架,实现了三维黏声最小二乘逆时偏移方法。采用极性编码技术大幅度降低黏声最小二乘逆时偏移算法的计算量与内存,使三维黏声最小二乘逆时偏移算法的实用化成为可能。在模型试算部分,首先通过Marmousi模型验证了该方法对传统声波最小二乘逆时偏移方法的优势,最后对三维含Q平层模型及盐丘模型进行试算,证明了三维黏声最小二乘逆时偏移算法的正确性。 相似文献
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Radon变换的MATLAB实现 总被引：4，自引：0，他引：4
Ｒａｄｏｎ变换（包括线性、抛物Ｒａｄｏｎ变换）是地震数据处理中的一种强有力工具。作者在文中阐述了其原理及最小平方算法,并给出了ＭＡＴＬＡＢ语言缩写的源码,试算结果表明了该算法的有效性和程序的正确性,该程序可直接当作工具来使用。 相似文献
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介绍了估计了质分维数的传统基因算法的主要步骤及改进的基因算法,并通过实例与传统的线性最小二乘法和非线性叠代法进行了比较,表明该方法直观,简便,通用性强,并具有较高的拟合精度。 相似文献
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在地质建模过程中,层位面与层位面之间,断层面与层位面之间经常需要求交运算.由于地震数据解释得到的层位面和断层面往往数据量非常庞大,因此研究时间复杂度、空间复杂度都很低的求交算法,具有很强的理论意义和实用价值.这里提出一种新算法,先构造两曲面的最小包围盒,求出相交部份,再对相交部份空间构造出平均单元格,将各三角形分配到平均单元格后进行求交,最后根据交线进行曲面分割.数值实验表明,该算法能正确地、高效地求交,并根据交线有效完成曲面分割. 相似文献
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土壤铅含量高光谱遥感反演中波段选择方法研究 总被引：7，自引：0，他引：7
利用高光谱遥感数据进行了南京郊外土壤重金属元素铅的含量反演,由于高光谱数据波段众多,波段选择或变换至关重要。比较了基于次贪婪的前向选择模型的最小角度拟合和基于遗传算法进行波段选择的最小二乘和偏最小二乘拟合,结果发现基于遗传算法的偏最小二乘反演结果优于全波段的偏最小二乘,表明波段选择在高光谱反演重金属中是有益的。尽管采取了波段选择后的各方法在反演时均能达到70%以上的训练精度,但因遗传算法搜索的解空间范围更宽广,使得基于遗传算法的偏最小二乘优于前向选择模型的最小角度拟合。最后还比较了基于遗传算法的普通最小二乘和偏最小二乘拟合,结果表明偏最小二乘更优,因此在高光谱反演重金属含量当中,偏最小二乘精度较高,而在波段选择方法中,遗传算法更优。 相似文献
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Application of Partial Least Squares Regression in Multimodal Integrated Forecasting of Water Vapor and Surface Air Temperature 总被引：1，自引：1，他引：0
The use of a new multi model integration method of Partial Least Squares regression (PLS) can completely eliminate the multicollinearity features to improve multi model’s integrated forecasting results of the humidity and temperature. Based on the four centers’ ensemble forecast results, namely, the European Center for Medium-Range Weather Forecasts (ECMWF), Chinese Meteorological Administration (CMA), the Japan Meteorological Agency (JMA) and the UK Met Office (UKMO), we built a 2012 multi mode (25°~60°N, 60°~150°E) 24 ~168 hours forecast time (interval 24 hours) multi model for humidity and temperature and used the four methods, like ensemble average (BREM) for eliminating the deviation, a simple set of average (EMN), Super Ensemble (SUP) and Partial Least Squares regression (PLS) for ground temperature multi model integration. We used the Root-Mean-Square Error (RMSE) and anomaly correlation coefficient (cor) to determine the effect of more modes of integration and to predict a short course of cold. The two prediction results showed that the Partial Least Squares regression (PLS) was the best multi model integrated method, more superior than the other three single modes and compared with the other three methods, it showed better prediction performance, which has certain value and application prospect. 相似文献
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Pijush Samui Tim Lansivaara Madhav R. Bhatt 《Geotechnical and Geological Engineering》2013,31(4):1329-1334
This paper investigates the feasibility of Least square support vector machine (LSSVM) model to cope the problem of implicit performance function during first order second moment (FOSM) method based slope reliability analysis. LSSVM is firmly based on the theory of statistical learning. In LSSVM, Vapnik’s ε -insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. Here, LSSVM has been used as a regression technique to approximate implicit performance functions. A slope example has been presented for illustrating the applicability of LSSVM based FOSM method. The developed LSSVM based FOSM has been compared with the artificial neural network (ANN) and least square method. The result shows that the approximation of LSSVM can be used in the FOSM method for slope reliability analysis. 相似文献
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通过对负指数分布模型函数的分析,推导了负指数分布的极大值函数,证明在岩体结构面实测数据的回归分析中,负指数分布存在局限性。改用双参数负指数分布作为模型函数后,能克服负指数分布的缺点,继承其优点,提高回归分析的拟合度。回归分析的方法使用最小二乘法曲线回归方法。在云南小湾水电站坝基槽边坡结构面的统计中,双参数负指数分布作为回归分析的模型函数得到了成功运用。 相似文献
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最小二乘支持向量机方法(LSSVM)在处理小样本、高维数、非线性的问题时,具有求解速度快、易于描述非线性关系的优良特性。但是,该方法得到的模型拟合精度和泛化能力依赖于其相关参数,因此,提出基于粒子群优化算法(PSO)的LSSVM参数优选方法。最后,用该模型对巷道围岩松动圈进行了预测研究。结果表明,PSO优化的LSSVM模型具有收敛速度快、计算精度高的特点,说明该模型是合理、有效的。 相似文献
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Geodesy utilizes state of the art data collection techniques such as GPS (Global Positioning System) to acquire locations of points. Traditionally, the coordinates of these points are estimated using the Least Squares (LS) method. Nevertheless, Robust Estimation (RE) yields more accurate results than LS method in the presence of blunders (gross errors) among the data set. For example, the Least Trimmed Squares (LTS) method and the Least Median Squares (LMS) method can be used for this purpose. The first method aims to minimize the sum of the squared residuals by trimming away observations with large residuals. On the other hand, the second method involves the minimization of the median of the squared residuals. Both methods can be implemented using an optimization method, i.e., Artificial Bee Colony (ABC) algorithm. The ABC algorithm is a swarm intelligence (a branch of artificial intelligence) technique that can be used for the solution of minimization or maximization problems. In this paper, using the LTS and LMS methods for GPS data by employing the ABC, a new approach is put forward. Firstly, some discussions about the theoretical principals of RE and ABC are given. Then, a numerical example is used to demonstrate the validity of the proposed approach. Numerical results show that application of the robust estimation to GPS data can easily be carried out by ABC and this approach helps to enhance the reliability of geospatial data for any application of geodesy. 相似文献
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Sediment contaminant concentrations usually show an inverse correlation with grain size. This can cause difficulties in distinguishing real differences in contamination from artifacts caused by variations in sediment texture. To overcome this, regression analysis is frequently used to remove the dependency of concentrations on grain size. However, least squares regression lines can be affected markedly by the presence of a small number of unusual samples in the dataset. These outliers may represent samples which are more severely contaminated or which were derived from areas with different underlying geology. They can be removed semi-manually, but robust regression methods such as least absolute values provide a convenient and objective alternative. The methods are illustrated using an example dataset of metal contaminants in sediments from the Humber Estuary, United Kingdom. Least squares regression on the complete dataset yields a rather poor grain size normalization for several elements. By contrast, least absolute values regression produces results very similar to those obtained by least squares regression after careful manual removal of outliers, but it avoids the need for subjective judgments of which data points to omit from the analysis. The intercepts of several of the fitted regression lines were non-zero, indicating that regression-based normalization is preferable to methods based on ratios. 相似文献
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随着煤层气勘探的不断深入,对煤层含气量预测精度提出了更高的要求。基于煤层含气量测井响应特征,分析测井参数与含气量的相关性,提出MIV（Mean Impact Value）技术与LSSVM（Least Squares Support Vector Machine）结合的测井参数优选策略,优选最优测井参数作为网络建模的输入自变量组合,通过粒子群算法优化LSSVM网络核心参数,最后构建一套适用于煤层含气量预测的MIV-PSO-LSSVM模型。在此基础上,分别对比分析LSSVM、PSO-LSSVM、MIV-LSSVM和MIV-PSO-LSSVM模型对煤层含气量的预测性能,并与传统多元回归方法进行了对比,利用拟合优度和均方根误差对此5类模型进行评价。结果表明:PSO优化下的LSSVM模型预测精度得到有效提升,结合MIV方法优选测井参数可大幅度改善神经网络建模性能,MIV-PSO-LSSVM模型可实现煤层含气量高精度预测,为煤层气勘探及其储层评价提供新的技术支撑,且本研究的建模策略及思想可广泛应用于其他机器学习建模研究领域。 相似文献