Local and parallel finite element algorithm for stationary incompressible magnetohydrodynamics

This article presents a local and parallel finite element method for the stationary incompressible magnetohydrodynamics problem. The key idea of this algorithm comes from the two‐grid discretization technique. Specifically, we solve the nonlinear system on a global coarse mesh, and then solve a series of linear problems on several subdomains in parallel. Furthermore, local a priori estimates are obtained on a general shape regular grid. The efficiency of the algorithm is also illustrated by some numerical experiments.© 2017 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 33: 1513–1539, 2017     

A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

The self‐adaptive intelligence gray predictive model (SAIGM) has an alterable‐flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.     

Proximal-gradient algorithms for fractional programming

In this paper, we propose two proximal-gradient algorithms for fractional programming problems in real Hilbert spaces, where the numerator is a proper, convex and lower semicontinuous function and the denominator is a smooth function, either concave or convex. In the iterative schemes, we perform a proximal step with respect to the nonsmooth numerator and a gradient step with respect to the smooth denominator. The algorithm in case of a concave denominator has the particularity that it generates sequences which approach both the (global) optimal solutions set and the optimal objective value of the underlying fractional programming problem. In case of a convex denominator the numerical scheme approaches the set of critical points of the objective function, provided the latter satisfies the Kurdyka-?ojasiewicz property.     

广义线性多乘积问题的完全多项式时间近似算法

本文针对广义线性多乘积极小化问题,通过一系列的线性规划问题的解提出一种求其全局最优解的完全多项式时间近似算法,并给出该算法的计算复杂性,且数值算例验证该算法是可行的.     

一种新的求非线性方程组的数值延拓法

针对迭代过程中的Jacobi奇异问题,本文提出了一种新的数值延拓法.通过构造双参数同伦算子,采用可控条件和适当选取参数的方式克服Jacobi奇异性,并分析了方法的收敛性.最后,通过数值实验对比,验证了方法的可行性和优越性.特别是具有可调控越过Jacobi奇异(点、线、面)的优势,从而也在某种程度上解决了数值延拓法严重依赖于初值的问题.     

阿拉尔垦区自然灾害对农业经济影响的研究

阿拉尔垦区是新疆重要的棉花、林果产业基地,然而频发的自然灾害给农业经济造成了极为严重的影响.自然灾害发生频率高、灾害地域分布不均衡以及带来的破坏效果大和损失严重等特点,引起了人们的广泛关注.采用层次分析法作为基础理论,通过专家打分法确定权重建立评价模型.阿拉尔垦区自然灾害对农业经济影响的研究是一个非常重要的理论和实践问题,它对于提高农民收入、促进农业可持续发展和社会稳定具有重要的意义.     

自适应多种群回溯群居蜘蛛算法求解TSP问题

在群居蜘蛛优化算法中引入自适应决策半径,将蜘蛛种群动态地分成多个种群,种群内适应度不同的个体采取不同的更新方式.在筛选全局极值的基础上,根据进化程度执行回溯迭代更新,提出一种自适应多种群回溯群居蜘蛛优化算法,旨在提高种群样本多样性和算法全局寻优能力.函数寻优结果表明改进算法具有较快的收敛速度和较高的收敛精度.最后将其应用于TSP问题的求解.     

基于改进的PSO-SVM的音频信号特征识别和分类

针对音频信号准确性分类的问题,提出一种基于改进的的粒子群优化算法(PSO)的支持向量机(SVM)音频信号分类的方法,简称IPSO-SVM.首先用Mel倒谱系数法对4种音频信号进行特征提取.其次在PSO中引入自适应变异因子,能够成功地跳出局部极小值点;然后对PSO中的惯性权重进行了改进,将惯性权重由常数变为指数型递减函数.随着迭代的进行,使权重逐渐减小,这样做有利于粒子进行局部寻优.最后用改进的PSO不断优化SVM中的惩罚因子c和核函数参数g来提高预测精度.实验结果表明,与传统的SVM、PSO-SVM、GA-SVM相比,我们提出的IPSO-SVM算法分类结果更精确.     

SWEIA艾滋病毒传染模型及应用

人类免疫缺陷病毒(HIV)是一种严重威胁生命的病毒,感染艾滋病毒患者一般经历四个阶段:i)艾滋病毒阴性的窗口期(W);ii)阳性的无症状潜伏期(E);iii)有症状期(Ⅰ);以及iv)移除阶段(A).为深入研究艾滋病传播过程,建立SWEIA艾滋病毒传染模型,定义基本再生数,分析无病与地方病平衡点的存在性和局部稳定性,根据2004至2015年中国艾滋病患者数据,采用遗传算法对SWEIA模型中参数进行估计.通过对基本再生数敏感性分析以及模型数值随参数不同而产生的变化,揭示艾滋病窗口期的接触率是影响艾滋病流行的主要原因之一.