Supply Chain Finance Credit Risk Evaluation Method Based on Self-Adaption Weight

Credit risk is the core issue of supply chain finance. In the supply chain, problems happened in different enterprises can influent the whole to different degrees through transferring, thus statuses of all enterprises and their different influences should be considered when evaluating the supply chain’s credit risk. We examine the characters of supply chain network and complex network, use the local growing complex network to simulate the real supply chain, use cluster analysis to classify the company into several levels;Introducing each level’s self-adaption weight formula according to the company’s quantity and degrees of this level and use the weight to improve the credit evaluation method. The research results indicate that complex network can be used to simulate the supply chain. The credit risk evaluation (CRE) of an enterprise level with bigger note degrees has a greater weight in the supply chain system’s CRE, thus has greater effect on the whole chain. Considering different influences of different enterprise levels can improve credit risk evaluation method’s sensitivity.

Enhanced self-adaptive evolutionary algorithm for numerical optimization

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.

Bi-Criteria Optimization Technique in Stochastic System Maintenance Allocation Problem

In this paper, the problem of optimum allocation of repairable and replaceable components in a system is formulated as a Bi-objective stochastic non linear programming problem. The system maintenance time and cost are random variable and has gamma and normal distribution respectively. A Bi-criteria optimization technique, weighted Tchebycheff is used to obtain the optimum allocation for a system. A numerical example is also presented to illustrate the computational details.

Algorithms and statistical analysis for linear structured weighted total least squares problem

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.However,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix.It is called the linear structured EIV(LSEIV)model.Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications.On the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)model.On the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix.The algorithms are derived through the Lagrange multipliers method and linear approximation.The estimation principles and iterative formula of the parameters are proven to be consistent.The first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach.Afterwards,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and effective.Finally,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

Reservoir parameter inversion based on weighted statistics

Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.

An Explicit Integer Programming Model of the Minimal Spanning Tree Problem for Digraphs with Asymmetric Weights

As far as the minimal spanning tree problem for the digraph with asymmetric weightsis concerned, an explicit integer programming model is proposed, which could be solved successfullyusing the integer programming packages such as LINDO, and furthermore this model is extendedinto the stochastic version, that is, the minimal spanning tree problem for the digraph with theweights is not constant but random variables. Several algorithms are also developed to solve themodels. Finally, a numerical demonstration is given.

THE ASYMPTOTIC PROPERTIES OF WEIGHTED MARKOV OPERATORS

Let (X, ∑, μ) be a σ-finite measure space, P : LI → L1 be a Markov operator, and Qt = ∑n=0 ∞ qn(t)Pn, where {qn(t)} be a sequence satisfying:i) qn(t) ≥ 0 and ∑n=0 ∞ qn(t)=1 for all t >0;ii)lim (q0(t) + ∑n=1 ∞ |qn(t) -qn-1(t)|) = 0.t→∞f ∈ L1, it is proved that Qt(f) convergent strongly to a fixed point of P as t → 0 if and only if {Qt(f)}t>0 is precompact. Qt(f) is convergent if and only if the ergodic mean operator An(f) is convergent, and they have the same limit. If P is a double stochastic operator then lim Qtf = E(f|∑0) for all f ∈ L1, where ∑0 is the invariant σ-algebra ofP. Some related results are also given.

区间粗糙数群组G1法的随机聚合求解及应用

针对复杂不确定环境下的群体评价问题,采用区间粗糙数表征专家偏好,在G1法的基础上,结合蒙特卡洛仿真技术,提出了一种随机模拟聚合算法.首先,通过随机抽样的方式模拟权重系数,并依据序相关性和区间粗糙数贴近度确定专家权重.其次,综合所有专家意见得到指标的最终权重,并将其与预处理后的指标值线性集结,求得一次模拟的综合评价值,据此判断被评价对象间的优劣.通过充分模拟得出优胜度矩阵,并从中推导出包含优胜概率的可能性排序,弥补了不确定信息环境下绝对排序呈现的不足.最后,通过算例说明了方法的有效性,并与现有方法对比阐述了所提方法的优势.

一种智能手机GNSS精密定位随机模型构建方法及定位性能分析

全球导航卫星系统(GNSS)定位芯片已经成为智能手机中实现导航定位的重要传感器,随着信息化、智能化社会的不断发展和深入,人们对手机高精度定位服务的需求不断增加。在分析伪距残差与高度角、载噪比(C/N_(0))及手机GNSS原始观测信息的相关性时发现:相比于C/N_(0),手机GNSS原始观测信息中的ReceivedSvTimeUncertaintyNanos与伪距残差的相关性更高。因此,提出了一种基于岭回归和智能手机原始观测信息的GNSS精密定位随机模型构建方法。通过构建以手机原始观测信息为特征变量的岭回归模型,利用谷歌公开数据集训练该模型,学习伪距残差与各种特征变量之间的映射关系,进而在线预测伪距噪声,以此作为GNSS定位中观测值定权的依据。最后,进行实时动态定位(RTK)和精密单点定位(PPP)实验以验证新定权方法的定位性能,实验结果表明,相比传统C/N_(0)定权方法,新定权方法的定位精度在3组实验中平均提升幅度分别为24.9%,44.8%及39.3%。

基于MIWOA优化SCN的变压器故障诊断研究

针对变压器故障诊断精确度低的问题,本文提出了一种多策略改进的鲸鱼优化算法(MIWOA)优化随机配置网络(SCN)的变压器故障诊断模型。首先,对变压器冗杂繁多的原始故障数据进行核主成分分析(KPCA)降维处理,降低无效特征的影响;其次,利用Tent混沌映射、动态自适应权重和初级知识获取共享算法对鲸鱼算法(WOA)进行改进,提高其优化能力;然后,在SCN中引入L2范数惩罚项进行正则化处理,并使用改进后的MIWOA算法对SCN惩罚项系数C进行寻优求解,提高SCN分类精度和泛化能力;最后,将降维的数据输入到MIWOA-SCN故障诊断模型中,提高模型收敛速度。结果表明,本文所提出的模型诊断精度为93.1%,与WOA-SCN、GWO-SCN和PSO-SCN诊断模型相比,分别提高了6.89%、9.48%、14.65%,证明MIWOA-SCN诊断模型在变压器故障诊断上具有良好的诊断效果。

随机扰动下具有分布式时滞的多权重网络指数同步

研究随机扰动下具有分布式时滞的多权重网络指数同步问题.鉴于分布式时滞和随机扰动给控制系统所带来的不确定性以及多权重网络具有的灵活性和普适性,通过设计有效的控制协议,利用伊藤引理和不等式技巧,选取合适的Lyapunov函数,得到多权重网络实现指数同步的充分性条件,并通过数值仿真验证理论结果的有效性.

基于拓扑信息和特征属性的社区发现算法

文章提出一种新颖的加权算法,旨在提高社区检测算法准确性。该算法结合网络拓扑信息和节点属性信息,采用光谱聚类技术来揭示潜在的社区结构。它能够自适应地学习不同属性对检测任务的贡献度并相应地分配权重,同时采用软阈值算子和L1正则化项来降低“噪声”属性对检测精度的不利影响。通过对数值模拟和真实世界数据集的测试验证该算法在社区检测应用中的有效性。

Research of stochastic weight strategy for extended particle swarm optimizer

To improve the performance of extended particle swarm optimizer, a novel means of stochastic weight deployment is proposed for the iterative equation of velocity updation. In this scheme, one of the weights is specified to a random number within the range of [0, 1] and the other two remain constant configurations. The simulations show that this weight strategy outperforms the previous deterministic approach with respect to success rate and convergence speed. The experiments also reveal that if the weight for global best neighbor is specified to a stochastic number, extended particle swarm optimizer achieves high and robust performance on the given multi-modal function.

Stochastic comparisons on total capacity of weighted k-out-of-n systems with heterogeneous components

This paper carries out stochastic comparisons on the total capacity of weighted k-out-of-n sys-tems with heterogeneous components.The expectation order,the increasing convex/concave order and the usual stochastic order are employed to investigate stochastic behaviours of sys-tem capacity.Sufficient conditions are established in terms of majorisation-type orders between the vectors of component lifetime distribution parameters and the vectors of weights.Some examples are also provided as illustrations.

高层结构基于稳定极限状态的整体抗震可靠度分析及应用

对于高层建筑结构,重力二阶效应对结构稳定性的不利影响显著。建立了基于等效刚重比退化率的整体稳定能力极限状态方程,提出了基于随机响应面法的整体抗震可靠度分析方法。以框架-核心筒结构为例进行整体稳定能力极限状态抗震可靠度分析,并与当前设计规范相适应的变形能力抗震可靠度分析结果进行对比。结果表明:整体稳定能力抗震可靠度指标可以有效地评估结构的抗震可靠性,与变形能力抗震可靠度指标具有较好的一致性。考虑材料强度和地震作用随机性,提出的方法可应用于高层建筑设计结构方案比选、既有高层结构的可靠性鉴定分析以及对加固方案有效性的评估。

基于随机加权法的BAYES精度评定

从工程应用的角度出发 ,在极小现场子样条件下 ,讨论了如何利用验前信息与现场子样来对导弹的命中精度进行评定 ,将随机加权法与BAYES方法结合起来 ,提出了基于随机加权法的BAYES精度评定方法 。

冲击信号的随机共振自适应检测方法

针对冲击信号的检测问题,在分析峭度指标和互相关系数衡量冲击信号的优势与不足的基础上,构造加权峭度指标。该指标兼顾两者的优势,弥补它们各自的不足。将加权峭度指标与变尺度随机共振相结合,提出了一种冲击信号的自适应随机共振检测算法,该算法被用于仿真冲击信号与实际工程中的微弱冲击信号的检测,结果表明所提方法有效提高了信噪比,还避免了漏峰现象。同时,所提的优化指标与随机共振算法独立,对其他冲击信号自适应算法也有参考价值。

基于AHP的模糊权重评定方法

层次分析法是一种定性与定量相结合的有效的评定方法。定性问题的量化处理本身具有不确定性,为提高层次分析法量化处理结果的可信度,结合计算机技术,对量化处理结果进行适当的模糊化处理。结合实例分析,模糊化处理获得了很好的结果。该方法简单、易于操作,便于工程应用。

加权马尔可夫链在伤寒副伤寒发病情况预测分析中的应用

目的研究加权马尔可夫链在伤寒副伤寒发病情况预测中的应用。方法用1991年1月至2000年10月江苏省伤寒副伤寒发病资料建立模型,以2000年11月和12月的发病资料作为模型预测效果的考核样本。首先基于伤寒副伤寒的发病情况存在大量不确定性的特点,应用有序聚类的方法建立发病人数状态的分级标准;然后针对发病人数序列为相依随机变量的特点,采取以规范化的各阶自相关系数为权重,用加权的马尔可夫链模型来预测和分析发病人数的变化状况,使预测结论的长期效果趋于最优;最后对预测结果进行分析和评价。结果江苏省伤寒副伤寒的发病趋势将在以后一段时间内仍呈平稳下降,检验结果表明模型是合适的。结论用加权马尔可夫链对伤寒副伤寒月发病情况的拟合结果满意,预测效果良好。

采用粒子群算法的冲击信号自适应单稳态随机共振检测方法

针对冲击信号阱内共振的特点、随机共振系统参数合理选取缺乏有效的理论依据以及传统自适应随机共振单参数优化的不足,提出一种基于粒子群算法(Particle swarm optimization,PSO)的多参数同步优化自适应单稳态随机共振方法。该方法采用单稳态随机共振模型,避免了经典双稳系统势阱间的共振跃迁,并选用单稳态系统输出的加权峭度指标作为PSO的适应度函数,能够实现多参数的同步自适应选取,从而最优地检测出原始信号中的冲击成分。将PSO和变尺度随机共振相结合,并给出冲击信号自适应随机共振算法流程。该算法被用于仿真冲击信号与工程实际中冲击信号的检测,结果表明所提方法简单易行,收敛速度快,能够有效提高信噪比,具有良好的工程应用前景。