支持向量机及其在提高采收率潜力预测中的应用

提高采收率潜力分析的基础是进行提高采收率方法的潜力预测 .建立提高采收率潜力预测模型从统计学习的角度来看 ,实质是属于函数逼近问题 .本文首次将统计学习理论及支持向量机方法引入提高采收率方法的潜力预测中 .根据 Vapnik结构风险最小化原则 ,应尽量提高学习机的泛化能力 ,即由有效的训练集样本得到的小的误差能够保证对独立的测试集仍保持小的误差 .在本文所用较少样本条件下 ,支持向量机方法能够兼顾模型的通用性和推广性 ,具有较好的应用前景 .研究中采用的是综合正交设计法、油藏数值模拟和经济评价等方法生成的理论样本集     

Non-Probabilistic Structural Reliability Analysis Integrating the PSO and the Kriging Model北大核心CSCD

针对复杂结构可靠性分析中面临的隐式功能函数和小样本问题,提出了一种粒子群优化和Kriging模型相结合的结构非概率可靠性分析方法。采用多维椭球描述结构不确定参数,运用粒子群优化对模型相关参数进行求解,并构建隐式功能函数的Kriging模型进行可靠性分析。三个算例结果表明所提方法有效可行,精度和效率均优于基于Kriging模型的非概率可靠性分析方法。     

基于灰色关联分析的支持向量机的铁路货运量预测研究

采用基于灰色关联分析的支持向量机对铁路货运量进行预测.首先利用灰色关联分析法对影响铁路货运量的因素进行分析处理,然后利用基于高斯核函数的支持向量回归机建立了铁路货运量预测模型.通过分析预测结果可以发现,经过灰色关联分析后的支持向量机模型对复杂的铁路货运量数据有较好地处理能力,且预测相对误差较小.特别地,由于支持向量机的适应性,该模型具有较高的泛化能力,对影响因素较为复杂,样本数量小的预测问题可以提供一定参考.     

一种新的光滑支持向量机的构造及其应用

光滑支持向量机模型是一个无约束、可微的最优化模型,人们可应用快速的最优化方法求解,从而降低计算复杂性.在前人工作的基础上研究基于样条函数的光滑支持向量机,采用广义三弯矩方法构造出六次样条光滑函数,分析了其性能及与正号函数的逼近精度,实现了求解六次样条光滑支持向量机的算法,与其它光滑支持向量机进行了比较,取得了较好的结果.最后将其应用于心脏病模型诊断,实验结果显示具有较高的精确度.     

装备费用预测中的混合核函数支持向量机

在支持向量机预测建模中,核函数用来将低维特征空间中的非线性问题映射为高维特征空间中的线性问题.核函数的特征对于支持向量机的学习和预测都有很重要的影响.考虑到两种典型核函数—全局核(多项式核函数)和局部核(RBF核函数)在拟合与泛化方面的特性,采用了一种基于混合核函数的支持向量机方法用于预测建模.为了评价不同核函数的建模效果、得到更好的预测性能,采用遗传算法自适应进化支持向量机模型的各项参数,并将其应用于装备费用预测的实际问题中.实际计算表明采用混合核函数的支持向量机较单一核函数时有更好的预测性能,可以作为一种有效的预测建模方法在装备管理中推广应用.     

基于灰色关联分析的模糊支持向量机中隶属度的确定

本文用灰色关联分析来替代模糊隶属度的求解,提出了一种新的有效地刻画样本不确定性的隶属度计算方法,理论上表明它是解决模糊支持向量机方法中一般使用特征空间中样本与类中心之间的距离关系构建隶属度函数的不足的方法之一,在一些特定条件下分类性能要强一些.     

基于主成分分析的支持向量机模型对上海房价的预测研究

采用基于主成分分析的支持向量机方法对上海房价进行预测.首先利用主成分分析法对原始数据进行降维处理,然后利用具有高水平的小样本学习能力的支持向量机进行预测模型的建立,对上海房价进行预测.实证显示,经过主成分分析的支持向量机模型能够较好地处理复杂的房地产数据,具有较高的预测能力,为上海房地产业的发展提供参考.特别地,该模型可以普遍应用于影响因素众多,时效性较强的短期小样本数据问题的预测,具有较高的泛化能力和很好的预测精度.     

一类基于L0/1软间隔损失函数的低秩支持张量机

支持向量机作为基于向量空间的一种传统的机器学习方法,不能直接处理张量类型的数据,否则不仅破坏数据的空间结构,还会造成维度灾难及小样本问题。作为支持向量机的一种高阶推广,用于处理张量数据分类的支持张量机已经引起众多学者的关注,并应用于遥感成像、视频分析、金融、故障诊断等多个领域。与支持向量机类似,已有的支持张量机模型中采用的损失函数多为L_0/1函数的代理函数。将直接使用L_0/1这一本原函数作为损失函数,并利用张量数据的低秩性,建立针对二分类问题的低秩支持张量机模型。针对这一非凸非连续的张量优化问题,设计交替方向乘子法进行求解,并通过对模拟数据和真实数据进行数值实验,验证模型与算法的有效性。     

支持向量机回归方法在地表水水质评价中的应用

将支持向量机方法应用于地表水质评价问题中,建立了多指标水质综合评价的支持向量机回归模型.在地表水质评价标准的基础上采用内插法获得学习样本,经过训练,得到水质评价的分类区间;然后以实测资料对所建模型进行检验,研究结果表明,支持向量机回归模型性能良好、预测精度高、简便易行,是水质评价的一种有效方法,具有广阔的应用前景.     

大气中臭氧含量分析预测的支向量机模型

以俄亥俄州(O h io)的气象、臭氧监测数据为基础,对一个监测点数据进行了分析处理,运用支持向量机回归方法,对气象指标的多参数样本进行学习,获得精确的支持向量机映射关系,并对臭氧含量进行预测.预测结果的误差较小,符合实际情况,能够较好的解决实际问题,说明支持向量机回归在预测上具有小的结构风险与强的泛化能力.     

M5 model tree and Monte Carlo simulation for efficient structural reliability analysis

Practically, the performance of many engineering problems can be defined using a complex implicit limit state function. Approximation of the accurate failure probability is very time-consuming and inefficient based on Monte Carlo simulation (MCS) for complex performance functions. M5 model tree (M5Tree) model is robust approach for simulation and prediction phenomena, which provides ability to dealing with complex implicit problems by dividing them into smaller problems. By improving the efficiency of reliability method using accurate approximated failure probability, an efficient reliability method using the MCS and M5Tree is proposed to calibrate the performance function and estimate the failure probability, respectively. The superiorities including simplicity and accuracy of M5Tree meta-model are investigated to evaluate the actual performance function through five nonlinear complex mathematical and structural reliability problems. The proposed reliability method-based MCS and M5Tree improved the computational efforts for evaluating the performance function in reliability analysis. The M5Tree significantly increased the efficiency of reliability analysis with accurate failure probability.     

A hybrid conjugate finite-step length method for robust and efficient reliability analysis

The robustness and efficiency of the first-order reliability method (FORM) are the important issues in the structural reliability analysis. In this paper, a hybrid conjugate search direction with finite-step length is proposed to improve the efficiency and robustness of FORM, namely hybrid conjugate finite-step length (CFSL-H). The conjugate scalar factor in CFSL-H is adaptively updated using two conjugate methods with a dynamic participation factor. The accuracy, efficiency and robustness of the CFSL-H are illustrated through the nonlinear explicit and structural implicit limit state functions with normal and non-normal random variables. The results illustrated that the proposed CFSL-H algorithm is more robust, efficient and accurate than the modified existing FORM algorithms for complex structural problems.     

Chaos control for numerical instability of first order reliability method

The HL-RF algorithm of the first order reliability method (FORM) is a kind of popular iterative algorithm for solving the reliability index in structural reliability analysis and reliability-based design optimization. However, there are the phenomena of convergence failure such as periodic oscillation, bifurcation and chaos in the FORM for some nonlinear problems. This paper suggests a novel method to overcome the numerical instabilities of HL-RF algorithm of FORM based on the principle of chaos control. The essential causes of chaotic dynamics for numerical instabilities including periodic oscillation and chaos of iterative solutions of FORM are revealed. Moreover, the geometrical properties of periodic oscillation of the iterative formulas derived from the FORM and performance measure approach are analyzed and compared. Finally, the stability transformation method (STM) of chaos feedback control is proposed to implement the convergence control of FORM. Several numerical examples with explicit or implicit HL-RF iterative formulas illustrate that the STM is effective, simple and versatile, and can control the periodic oscillation, bifurcation and chaos of the FORM iterative algorithm.     

A new method for parameter sensitivity estimation in structural reliability analysis

For the parameter sensitivity estimation with implicit limit state functions in the time-invariant reliability analysis, the common Monte Carlo simulation based approach involves multiple trials for each parameter being varied, which will increase associated computational cost and the cost may become inevitably high especially when many random variables are involved. Another effective approach for this problem is featured as constructing the equivalent limit state function (usually called response surface) and performing the estimation in FORM/SORM. However, as the equivalent limit state function is polynomial in the traditional response surface method, it is not a good approximation especially for some highly non-linear limit state functions. To solve the above two problems, a new method, support vector regression based response surface method, is therefore presented in this paper. The support vector regression algorithm is employed to construct the equivalent limit state function and FORM/SORM is used in the parameter sensitivity estimation, and then two illustrative examples are given. It is shown that the computational cost of the sensitivity estimation can be greatly reduced and the accuracy can be retained, and results of the sensitivity estimation obtained by the proposed method are in satisfactory agreement with those computed by the conventional Monte Carlo methods.     

Establishment of non-negative constraint method as a robust and efficient first-order reliability method

In structural reliability analysis, computation of reliability index or probability of failure is the main purpose. The Hasofer–Lind and Rackwitz–Fiessler (HL-RF) method is a widely used method in the category of first-order reliability methods (FORM). However, this method cannot be trusted for highly nonlinear limit state functions. Two proposed methods of this paper replace the original real valued constraint of FORM with a non-negative constraint, in all steps and during the whole procedure. First, the non-negative constraint is directly used to construct a non-negative Lagrange function and a search direction vector. Then, the first- and second-order Taylor approximation of the non-negative constraint are employed to compute step sizes of the first and second proposed methods, respectively. Contribution of the non-negative constraint and the effective approach of determining step sizes have led to the efficient computation of reliability index in nonlinear problems. The robustness and efficiency of two proposed methods are shown in various mathematical and structural examples of the literature.     

Adaptive reliability analysis based on a support vector machine and its application to rock engineering

The response surface method (RSM), a simple and effective approximation technique, is widely used for reliability analysis in civil engineering. However, the traditional RSM needs a considerable number of samples and is computationally intensive and time-consuming for practical engineering problems with many variables. To overcome these problems, this study proposes a new approach that samples experimental points based on the difference between the last two trial design points. This new method constructs the response surface using a support vector machine (SVM); the SVM can build complex, nonlinear relations between random variables and approximate the performance function using fewer experimental points. This approach can reduce the number of experimental points and improve the efficiency and accuracy of reliability analysis. The advantages of the proposed method were verified using four examples involving random variables with different distributions and correlation structures. The results show that this approach can obtain the design point and reliability index with fewer experimental points and better accuracy. The proposed method was also employed to assess the reliability of a numerically modeled tunnel. The results indicate that this new method is applicable to practical, complex engineering problems such as rock engineering problems.     

A response-surface-based structural reliability analysis method by using non-probability convex model

Due to its weak dependence on the amount of the uncertainty information, the non-probability convex model approach can be used to deal with the problems without sufficient information. In this paper, by integrating the response surface (RS) technique with the convex model approach, a new structural reliability analysis method is developed for many complex engineering problems with black-box limit-state functions. Using the newly developed correlation analysis technique for non-probability convex model, the multi-dimensional ellipsoid is efficiently constructed to characterize the uncertain parameters. A quadratic polynomial without cross terms is adopted to parameterize the black-box limit-state function, based on which the functional values as well as the first-order gradients can be explicitly calculated. At each iteration, the created RS is combined with the iHL-RF algorithm to obtain an approximate reliability index. A sequential procedure is subsequently formulated to update the RS and hence improve the precision of the reliability analysis. Four numerical examples and one engineering application are investigated to demonstrate the effectiveness of the presented method.     

Reliability-Based Optimization of structural systems

A method to carry out a Reliability-Based Optimization (RBO) of especially nonlinear structural systems is introduced. Statistical uncertainties involving both structural and loading properties are considered. The concept is based on the separation of structural reliability analyses and the optimization procedures. Two approaches are discussed, depending on the interaction of reliability analysis and mathematical programming and the way of representation of the limit state functions (LSF) of the structure. As, for cases of practical significance, the LSF is known only pointwise it is approximated by Response Surfaces (RS). For the response calculations Finite Element (FE) procedures are utilized. Failure probabilities are determined by applying variance reducing Monte Carlo simulation (MCS) techniques such as Importance Sampling (IS). Following the reliability analysis, the optimization procedure is controlled by the NLPQL algorithm. A numerical example in terms of a template ocean platform exemplifies the procedures.Formerly Institute of Engineering Mechanics, Leopold-Franzens-University, Innsbruck, Austria     

An efficient third-moment saddlepoint approximation for probabilistic uncertainty analysis and reliability evaluation of structures

This paper presents an efficient third-moment saddlepoint approximation approach for probabilistic uncertainty analysis and reliability evaluation of random structures. By constructing a concise cumulant generating function (CGF) for the state variable according to its first three statistical moments, approximate probability density function and cumulative distribution function of the state variable, which may possess any types of distribution, are obtained analytically by using saddlepoint approximation technique. A convenient generalized procedure for structural reliability analysis is then presented. In the procedure, the simplicity of general moment matching method and the accuracy of saddlepoint approximation technique are integrated effectively. The main difference of the presented method from existing moment methods is that the presented method may provide more detailed information about the distribution of the state variable. The main difference of the presented method from existing saddlepoint approximation techniques is that it does not strictly require the existence of the CGFs of input random variables. With the advantages, the presented method is more convenient and can be used for reliability evaluation of uncertain structures where the concrete probability distributions of input random variables are known or unknown. It is illustrated and examined by five representative examples that the presented method is effective and feasible.