RMI方法在若干微积分重要概念和方法中的应用

从RMI方法的角度就一元微积分中"无穷级数收敛"、"瞬时速度"、"定积分"等重要概念以及"复合函数求导"、"反函数求导"、"求等价无穷小"等重要计算方法进行了分析.     

Concepts and fuzzy sets: Misunderstandings, misconceptions, and oversights

The psychology of concepts has been undergoing significant changes since the early 1970s, when the classical view of concepts was seriously challenged by convincing experimental evidence that conceptual categories never have sharp boundaries. Some researchers recognized already in the early 1970s that fuzzy set theory and fuzzy logic were potentially suitable for modeling of concepts and obtained encouraging results. This positive attitude abruptly changed in the early 1980s, and since that time fuzzy set theory and fuzzy logic have been portrayed as problematic and unsuitable for representing and dealing with concepts. Our aim in this paper is to identify some of the most notorious claims regarding fuzzy set theory and fuzzy logic that have propagated through the literature on psychology of concepts and to show that they are, by and large, false. We trace the origin and propagation of these claims within the literature in this area. It is shown in detail that these claims are consistently erroneous and that they are based on various misunderstandings, misconceptions, and oversights. The ultimate purpose of this paper is to document these various erroneous claims.     

Lattice-valued fuzzy Turing machines: Computing power, universality and efficiency

In this paper we study fuzzy Turing machines with membership degrees in distributive lattices, which we called them lattice-valued fuzzy Turing machines. First we give several formulations of lattice-valued fuzzy Turing machines, including in particular deterministic and non-deterministic lattice-valued fuzzy Turing machines (l-DTMcs and l-NTMs). We then show that l-DTMcs and l-NTMs are not equivalent as the acceptors of fuzzy languages. This contrasts sharply with classical Turing machines. Second, we show that lattice-valued fuzzy Turing machines can recognize n-r.e. sets in the sense of Bedregal and Figueira, the super-computing power of fuzzy Turing machines is established in the lattice-setting. Third, we show that the truth-valued lattice being finite is a necessary and sufficient condition for the existence of a universal lattice-valued fuzzy Turing machine. For an infinite distributive lattice with a compact metric, we also show that a universal fuzzy Turing machine exists in an approximate sense. This means, for any prescribed accuracy, there is a universal machine that can simulate any lattice-valued fuzzy Turing machine on it with the given accuracy. Finally, we introduce the notions of lattice-valued fuzzy polynomial time-bounded computation (lP) and lattice-valued non-deterministic fuzzy polynomial time-bounded computation (lNP), and investigate their connections with P and NP. We claim that lattice-valued fuzzy Turing machines are more efficient than classical Turing machines.     

Judicial productivity,delay and efficiency: A Directional Distance Function (DDF) approach

The aim of this paper is twofold. On the one hand, it provides a contribution to the debate on judicial efficiency by conducting an applied research on the Italian tax judiciary thanks to a database covering the activities of the Italian tax courts over a 3-year period (2009–2011). On the other hand, it also contributes to the methodological debate, as it compares results obtained with Data Envelopment Analysis (DEA) and Directional Distance Function (DDF), two related non-parametric techniques which allow evaluating the efficiency of each observation as the radial distance from the efficient frontier defined by the best observations. While DEA has already been used to assess the mere technical efficiency of judicial systems, the DDF offers a valuable additional contribution, since it makes it possible to minimize the social cost of production of adjudication in the measurement. This feature makes it particularly attractive in those sectors in which production externalities may arise, such as judicial delays in the case investigated here. Additionally, the paper first applies the bootstrap to the DDF procedure in order to provide more robust estimates and to compare them with the DEA results.     

Tractability frontiers in probabilistic team semantics and existential second-order logic over the reals

Probabilistic team semantics is a framework for logical analysis of probabilistic dependencies. Our focus is on the axiomatizability, complexity, and expressivity of probabilistic inclusion logic and its extensions. We identify a natural fragment of existential second-order logic with additive real arithmetic that captures exactly the expressivity of probabilistic inclusion logic. We furthermore relate these formalisms to linear programming, and doing so obtain PTIME data complexity for the logics. Moreover, on finite structures, we show that the full existential second-order logic with additive real arithmetic can only express NP properties. Lastly, we present a sound and complete axiomatization for probabilistic inclusion logic at the atomic level.     

A matheuristic for the truck and trailer routing problem

In the truck and trailer routing problems (TTRPs) a fleet of trucks and trailers serves a set of customers. Some customers with accessibility constraints must be served just by truck, while others can be served either by truck or by a complete vehicle (a truck pulling a trailer). We propose a simple, yet effective, two-phase matheuristic that uses the routes of the local optima of a hybrid GRASP × ILS as columns in a set-partitioning formulation of the TTRP. Using this matheuristic we solved both the classical TTRP with fixed fleet and the new variant with unlimited fleet. This matheuristic outperforms state-of-the-art methods both in terms of solution quality and computing time. While the best variant of the matheuristic found new best-known solutions for several test instances from the literature, the fastest variant of the matheuristic achieved results of comparable quality to those of all previous method from the literature with an average speed-up of at least 2.5.     

The Gleason theorem for the field of rational numbers and residue fields

Charges taking values in a fieldF and defined on orthomodular partially ordered sets (logics) of all projectors in some finite-dimensional linear space overF are considered. In the cases whereF is the field of rational numbers or a residue field, the Gleason representation , where is a linear operator, is proved.Translated fromMatematicheskie Zametki, Vol. 64, No. 4, pp. 584–591, October, 1998.This research was supported by the Russian Foundation for Basic Research under grant No. 96-01-01265.     

A bi‐level neural‐based fuzzy classification approach for credit scoring problems

The credit scoring is a risk evaluation task considered as a critical decision for financial institutions in order to avoid wrong decision that may result in huge amount of losses. Classification models are one of the most widely used groups of data mining approaches that greatly help decision makers and managers to reduce their credit risk of granting credits to customers instead of intuitive experience or portfolio management. Accuracy is one of the most important criteria in order to choose a credit‐scoring model; and hence, the researches directed at improving upon the effectiveness of credit scoring models have never been stopped. In this article, a hybrid binary classification model, namely FMLP, is proposed for credit scoring, based on the basic concepts of fuzzy logic and artificial neural networks (ANNs). In the proposed model, instead of crisp weights and biases, used in traditional multilayer perceptrons (MLPs), fuzzy numbers are used in order to better model of the uncertainties and complexities in financial data sets. Empirical results of three well‐known benchmark credit data sets indicate that hybrid proposed model outperforms its component and also other those classification models such as support vector machines (SVMs), K‐nearest neighbor (KNN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA). Therefore, it can be concluded that the proposed model can be an appropriate alternative tool for financial binary classification problems, especially in high uncertainty conditions. © 2013 Wiley Periodicals, Inc. Complexity 18: 46–57, 2013     

基于EEMD的LS-SVM和BP神经网络混合短期负荷预测

提出了基于总体平均经验模态分解(EEMD)、最小二乘支持向量机(LSSVM)和BP神经网络的实用综合短期负荷预测方法,进行电力系统短期负荷预测.首先运用EEMD方法将非平稳的负荷序列分解,然后根据分解后各分量的特点选用最佳的核函数,利用最小二乘支持向量机分别对各分量进行预测,最后对各分量预测结果采用BP神经网络重构得到最终的预测结果.对实测数据的分析表明基于该综合方法的电力系统短期负荷预测具有较高的精度.     

Battery state-of-charge estimator using the SVM technique

State-of-charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state-of-charge becomes an important issue in all battery applications including electric vehicles (EV), hybrid electric vehicles (HEV) or portable devices. The aim of this innovative study is to estimate the SOC of a high capacity lithium iron phosphate (LiFePO₄) battery cell from an experimental data-set obtained in the University of Oviedo Battery Laboratory (UOB Lab) using support vector machine (SVM) approach. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current, voltage or temperature. An accurate predictive model able to forecast the SOC in the short term is obtained. The agreement of the SVM model with the experimental data-set confirmed its good performance.     

Minimizing loss probability bounds for portfolio selection

In this paper, we derive a portfolio optimization model by minimizing upper and lower bounds of loss probability. These bounds are obtained under a nonparametric assumption of underlying return distribution by modifying the so-called generalization error bounds for the support vector machine, which has been developed in the field of statistical learning. Based on the bounds, two fractional programs are derived for constructing portfolios, where the numerator of the ratio in the objective includes the value-at-risk (VaR) or conditional value-at-risk (CVaR) while the denominator is any norm of portfolio vector. Depending on the parameter values in the model, the derived formulations can result in a nonconvex constrained optimization, and an algorithm for dealing with such a case is proposed. Some computational experiments are conducted on real stock market data, demonstrating that the CVaR-based fractional programming model outperforms the empirical probability minimization.     

基于支持向量机模型的建筑物沉降预测

针对建筑沉降发生的过程,采用支持向量机(SVM)模型对建筑物沉降进行预测.使用前期施工过程中的沉降观测数据作为训练样本集,建立现场动态沉降量预报模型.仿真试验和实践结果表明,模型与BP神经网络预测模型相比能够更准确地反映实际沉降过程,且满足精确性和适用性的要求.     

基于核主元提取的支持向量机辨识

将一种基于特征提取的ε-不灵敏支持向量机方法用于非线性系统辨识.对输入输出数据首先进行核主元特征提取,将特征提取后的数据作为支持向量机的训练数据.将该方法与基于主元特征提取的方法和直接应用ε-不灵敏支持向量机的方法进行含噪和不含噪情况下的仿真比较,结果表明,方法的拟合性能和抗干扰能力优于其他两种方法.     

基于ANP-SVR的高校新专业设置优化分析

随着新专业的设置问题越来越多地成为各个高校普遍面对的发展问题,人们逐渐意识到决策过程中的滞后性、盲目性、片面性问题给专业设置工作乃至于该专业的生命力和竞争力带来的负面影响.运用ANP-SVR算法深入分析了高校新专业设置过程中的主要问题及其内部包含的各种因素,利用10个专业进行建模分析,并利用SVR,算法对3个拟建专业进行回归分析,得到了理想结果.方法将主观决策数字化,为高校的决策者提供了一种解决问题的新方法.     

我国专利申请量的支持向量机预测模型研究

运用支持向量机(support vector machine,SVM)和浮点遗传算法相结合的方法对我国专利申请量进行预测。数据仿真显示支持向量机预测方法比人工神经网络和逻辑回归方法有更高的预测精度,结果显示运用浮点遗传算法参数选取的支持向量机方法对我国专利申请量进行预测是可行和有效的。     

基于最近边界向量的SVM增量学习算法

为了减少求支持向量过程中二次规划的复杂度,利用训练样本集的几何信息,选出两类中离另一类最近的边界向量集合,它是样本中最有可能成为支持向量的一部分,用它代替原样本集进行训练.对新增样本,若存在违反KKT条件的样本,只对这部分新样本进行学习.同时找出原样本中可能转化为支持向量的非支持向量样本.基于分析结果,提出了一种新的基于最近边界向量的增量式支持向量机学习算法.对标准数据集的实验结果表明,算法是可行的,有效的.     

基于变分不等式的支持向量机优化算法研究

由于标准支持向量机模型是一个二次规划问题,随着数据规模的增大,求解算法过程会越来越复杂.在K-SVCR算法结构的基础上,构造了严格凸的二次规划新模型,该模型的主要特点是可以将其一阶最优化条件转化为变分不等式问题,利用Fischer-Burmeister(FB)函数将互补问题转化为光滑方程组;建立光滑快速牛顿算法求解,并证明了该算法所产生的序列是全局收敛;利用标准数据集测试提出算法的有效性,在训练正确率和运行时间上与K-SVCR算法相比都有较好的表现,实验结果表明该算法可行且有效.     

Characterizing and Generating Bivariate Empirical Rank Distributions Satisfying Certain Positive Dependence Concepts

Abstract

This article introduces an approach for characterizing the classes of empirical distributions that satisfy certain positive dependence notions. Mathematically, this can be expressed as studying certain subsets of the class S_N of permutations of 1, …, N, where each subset corresponds to some positive dependence notions. Explicit techniques for it-eratively characterizing subsets of S_N that satisfy certain positive dependence concepts are obtained and various counting formulas are given. Based on these techniques, graph-theoretic methods are used to introduce new and more efficient algorithms for constructively generating and enumerating the elements of various of these subsets of S_N. For example, the class of positively quadrant dependent permutations in S_N is characterized in this fashion.     

Non-Uniformity and Generalised Sacks Splitting

We show that there do not exist computable functions f ₁(e, i), f ₂(e, i), g ₁(e, i), g ₂(e, i) such that for all e, i ∈ ω, (1) $ {\left( {W_{{f_{1} {\left( {e,i} \right)}}} - W_{{f_{2} {\left( {e,i} \right)}}} } \right)} \leqslant _{{\rm T}} {\left( {W_{e} - W_{i} } \right)}; $ (2) $ {\left( {W_{{g_{1} {\left( {e,i} \right)}}} - W_{{g_{2} {\left( {e,i} \right)}}} } \right)} \leqslant _{{\rm T}} {\left( {W_{e} - W_{i} } \right)}; $ (3) $ {\left( {W_{e} - W_{i} } \right)} \not\leqslant _{{\rm T}} {\left( {W_{{f_{1} {\left( {e,i} \right)}}} - W_{{f_{2} {\left( {e,i} \right)}}} } \right)} \oplus {\left( {W_{{g_{1} {\left( {e,i} \right)}}} - W_{{g_{2} {\left( {e,i} \right)}}} } \right)}; $ (4) $ {\left( {W_{e} - W_{i} } \right)} \not\leqslant _{{\rm T}} {\left( {W_{{f_{1} {\left( {e,i} \right)}}} - W_{{f_{2} {\left( {e,i} \right)}}} } \right)}{\text{unless}}{\left( {W_{e} - W_{i} } \right)} \leqslant _{{\rm T}} {\emptyset};{\text{and}} $ (5) $ {\left( {W_{e} - W_{i} } \right)} \leqslant _{{\rm T}} {\left( {W_{{g_{1} {\left( {e,i} \right)}}} - W_{{g_{2} {\left( {e,i} \right)}}} } \right)}{\text{unless}}{\left( {W_{e} - W_{i} } \right)} \leqslant _{{\rm T}} {\emptyset}. $ It follows that the splitting theorems of Sacks and Cooper cannot be combined uniformly.