一类传染性疾病动力学数学模型的参数反演

针对一类传染性疾病动力学数学模型的参数反演问题,提出了最佳摄动量算法.此算法是利用算子识别摄动法和线性化技术,建立的数值迭代方法.在MATLAB平台下对具体算例进行了程序实现和数值计算,验证了最佳摄动量法解决此类问题的可行性和有效性,反演得到的参数结果有助于我们分析和研究传染性疾病动力学模型,从而进一步预测和评估疫情.     

改进的最佳摄动量法及在反问题中的应用

给出了改进的最佳摄动量法,并应用在双曲型方程参数反演问题的求解中.由遗传算法借助交叉和变异算子控制全局搜索来获得参数的初始迭代值,代入最佳摄动量法求解出稳定的高精度数值解.     

对流弥散方程的时间依赖反应系数反演及应用

探讨了一维对流弥散方程的时间依赖反应系数函数的反演问题及其在一个土柱渗流试验中的应用.借助一个积分恒等式,讨论了正问题单调解的存在条件及反问题的数据相容性.进一步考虑一个扰动土柱试验模型模拟问题,应用一种最佳摄动量正则化算法,对反应系数函数进行了数值反演模拟,并应用于实际试验数据的反分析,反演重建结果不仅与相容性分析一致,而且与实际观测数据基本吻合.     

二维热传导方程源强识别反问题的数值求解

本文研究了一类含有时间变量热源的二维热传导方程.利用有限元方法给出了数值求解过程,并在已知热源位置的前提下,根据某点的温度观测值,利用插值方法,将源强识别问题转化为参数反演问题,通过微分进化方法结合最佳摄动量法对源强识别反问题进行了数值模拟,结果表明所提出的方法是可行有效的.     

求解流固耦合问题的一种四步分裂有限元算法

基于arbitrary Lagrangian Eulerian (ALE) 有限元方法,发展了一种求解流固耦合问题的弱耦合算法．将半隐式四步分裂有限元格式推广至求解ALE描述下的Navier-Stokes（N-S）方程,并在动量方程中引入迎风流线（streamline upwind/Petrov-Galerkin, SUPG）稳定项以消除对流引发的速度场数值振荡;采用Newmark-β法对结构方程进行时间离散;运用经典的Galerkin有限元法求解修正的Laplace方程以实现网格更新,每个计算步施加网格总变形量防止结构长时间、大位移运动时的网格质量恶化．运用上述算法对弹性支撑刚性圆柱体的流致振动问题进行了数值模拟,计算结果与已有结果相吻合,初步验证了该算法的正确性和有效性．     

二维非饱和土壤水分运动方程的径向基配点差分法

针对二维非饱和土壤水分运动方程,将径向基配点法结合差分法构造了一种新的数值算法.该算法先采用差分法处理非线性项,再利用径向基函数配点法的隐格式求解方程,避免了因非线性项的存在导致不能直接使用配点法的现象,并且证明了该算法解的存在唯一性.通过对非饱和土壤水分运动的数值模拟,并采用试验数据对新算法进行了验证,模拟结果与试验结果非常吻合,表明该算法实用、有效.同时,比较分析了不同径向基函数以及不同算法的模拟精度,结果表明,与MQ函数和Guass函数相比,新的径向基函数具有更好的模拟精度,且相对于有限差分法和有限元法,本文提出的方法具有一定的优越性.     

空间-时间分数阶变系数对流扩散方程微分阶数的数值反演

考虑终值数据条件下一维空间-时间分数阶变系数对流扩散方程中同时确定空间微分阶数与时间微分阶数的反问题.基于对空间-时间分数阶导数的离散,给出求解正问题的一个隐式差分格式,通过对系数矩阵谱半径的估计,证明差分格式的无条件稳定性和收敛性.联合最佳摄动量算法和同伦方法引入同伦正则化算法,应用一种单调下降的Sigmoid型传输函数作为同伦参数,对所提微分阶数反问题进行精确数据与扰动数据情形下的数值反演.结果表明同伦正则化算法对于空间-时问分数阶反常扩散的参数反演问题是有效的.     

地学信息挖掘中RPROP和分层动量增项自适应BP算法应用研究

采用RPROP和分层动量增项自适应BP算法,从最小误差、收敛速度和运算次数方面对地球化学信息进行了研究,并进行了比较分析;针对本区域的样本数据训练结果,确定采用分层动量自适应算法进行后续预测统计工作,为矿区靶区预测提供支持.     

重建极性连续统理论的基本定律和原理（I）——微极连续统

在对现有微极连续统理论已进行过再研究的基础上重新建立较为完整的微极连续统理论的基本均衡定律和方程体系．在此重建的新体系中不但考虑了由于动量引起的附加动量矩、面力引起的附加面矩和体力引起的附加体矩，而且还考虑了微角速度引起的附加速度，从而可以建立起耦合型的动量、动量矩和能量的均衡定律．从这些新的基本均衡定律可以很自然地推导出相应的局部和非局部均衡方程．通过对比可以清楚地看到这些新结果较之现有的结果都完整．     

不变形双滴的热毛细迁移及相互作用

对微重力下不变形双滴的非定常热毛细迁移运动进行了数值模拟,采用了有限差分方法对动量方程和能量方程进行离散,使用波前追踪法捕捉运动的不变形液滴界面．研究显示双滴的排列方式对它们的迁移规律和相互作用影响很大,其中影响任一个液滴运动的最主要的因素是另一个液滴的存在所引起的温度场的扰动．     

A heterogeneous cooperative parallel search of branch-and-bound method and tabu search algorithm

In this study, we present a heterogeneous cooperative parallel search that integrates branch-and-bound method and tabu search algorithm. These two algorithms perform searches in parallel and cooperate by asynchronously exchanging information about the best solutions found and new initial solutions for tabu search. The rapid production of a good solution from the tabu search process provides the branch-and-bound process with a better feasible solution to accelerate the elimination of subproblems that do not contain an optimal solution. The new initial solution produced from the subproblem with a least-cost lower bound of the branch-and-bound method suggests the best potential area for tabu search to explore. We use a master-slave model to reduce the complexity of communication and enhance the performance of data exchange. A branch-and-bound process is used as the master process to control the exchange of information and the termination of computation. Several tabu search processes are executed simultaneously as the slave processes and cooperate by asynchronously exchanging information on the best solutions found and the new initial solutions by the master process of branch-and-bound. Based on the computation experiments of solving traveling salesman problems (TSP), the proposed heterogeneous parallel search algorithm outperforms a conventional parallel branch-and-bound method and a conventional parallel tabu search. We also present the computational results showing the efficiency of heterogeneous cooperative parallel search when we use more processors to accelerate search time. Thus, the proposed heterogeneous parallel search algorithm achieves linear accelerations.     

PSOHS: an efficient two-stage approach for data clustering

Cluster analysis is an important task in data mining and refers to group a set of objects such that the similarities among objects within the same group are maximal while similarities among objects from different groups are minimal. The particle swarm optimization algorithm (PSO) is one of the famous metaheuristic optimization algorithms, which has been successfully applied to solve the clustering problem. However, it has two major shortcomings. The PSO algorithm converges rapidly during the initial stages of the search process, but near global optimum, the convergence speed will become very slow. Moreover, it may get trapped in local optimum if the global best and local best values are equal to the particle’s position over a certain number of iterations. In this paper we hybridized the PSO with a heuristic search algorithm to overcome the shortcomings of the PSO algorithm. In the proposed algorithm, called PSOHS, the particle swarm optimization is used to produce an initial solution to the clustering problem and then a heuristic search algorithm is applied to improve the quality of this solution by searching around it. The superiority of the proposed PSOHS clustering method, as compared to other popular methods for clustering problem is established for seven benchmark and real datasets including Iris, Wine, Crude Oil, Cancer, CMC, Glass and Vowel.     

A Modification to the New Version of the Price’s Algorithm for Continuous Global Optimization Problems

This paper presents an algorithm for finding a global minimum of a multimodal, multivariate and nondifferentiable function. The algorithm is a modification to the new version of the Price’s algorithm given in Brachetti et al. [J. Global Optim. 10, 165–184 (1997)]. Its distinguishing features include: (1) The number-theoretic method is applied to generate the initial population so that the points in the initial population are uniformly scattered, and therefore the algorithm could explore uniformly the region of interest at the initial iteration; (2) The simplified quadratic approximation with the three best points is employed to improve the local search ability and the accuracy of the minimum function value, and to reduce greatly the computational overhead of the algorithm. Two sets of experiments are carried out to illustrate the efficiency of the number-theoretic method and the simplified quadratic model separately. The proposed algorithm has also been compared with the original one by solving a wide set of benchmark problems. Numerical results show that the proposed algorithm requires a smaller number of function evaluations and, in many cases, yields a smaller or more accurate minimum function value. The algorithm can also be used to deal with the medium size optimization problems.     

A global best artificial bee colony algorithm for global optimization

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in the ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose a modified ABC algorithm (denoted as ABC/best), which is based on that each bee searches only around the best solution of the previous iteration in order to improve the exploitation. In addition, to enhance the global convergence, when producing the initial population and scout bees, both chaotic systems and opposition-based learning method are employed. Experiments are conducted on a set of 26 benchmark functions. The results demonstrate good performance of ABC/best in solving complex numerical optimization problems when compared with two ABC based algorithms.     

Application of honey-bee mating optimization algorithm on clustering

Cluster analysis is one of attractive data mining technique that use in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honey-bees are among the most closely studied social insects. Honey-bee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honey-bee. Honey-bee has been used to model agent-based systems. In this paper, we proposed application of honeybee mating optimization in clustering (HBMK-means). We compared HBMK-means with other heuristics algorithm in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known datasets. Our finding shows that the proposed algorithm works than the best one.     

利用分裂Bregman方法的图像恢复组合模型

ROF模型是图像恢复中的经典模型,具有保留图像边缘的优点,但同时也存在梯子现象.而利用二次范数fΩ｜▽u｜2 dxdy的模型可以避免梯子现象,但容易使图像变得模糊.针对两种方法的优缺点,提出了一种新的通过设置边缘检测开关函数的组合模型,在图像平坦区利用二次范数模型处理,而在强边缘处利用ROF模型处理,而且应用分裂的Br...     

Improved lower bounds for the early/tardy scheduling problem with no idle time

In this paper, we consider the single machine earliness/tardiness scheduling problem with no idle time. Two of the lower bounds previously developed for this problem are based on Lagrangean relaxation and the multiplier adjustment method, and require an initial sequence. We investigate the sensitivity of the lower bounds to the initial sequence, and experiment with different dispatch rules and some dominance conditions. The computational results show that it is possible to obtain improved lower bounds by using a better initial sequence. The lower bounds are also incorporated in a branch-and-bound algorithm, and the computational tests show that one of the new lower bounds has the best performance for larger instances.     

MEA-BP模型在遥感影像分类中的应用研究

遥感影像分类作为遥感技术的一个重要应用,对遥感技术的发展具有重要作用.针对遥感影像数据特点,在目前的非线性研究方法中主要用到的是BP神经网络模型.但是BP神经网络模型存在对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.因此,为了提高模型遥感影像分类精度,提出采用MEA-BP模型进行遥感影像数据分类.首先采用思维进化算法代替BP神经网络算法进行初始寻优,再用改进BP算法对优化的网络模型权阈值进一步精确优化,随后建立基于思维进化算法的BP神经网络分类模型,并将其应用到遥感影像数据分类研究中.仿真结果表明,新模型有效提高了遥感影像分类准确性,为遥感影像分类提出了一种新的方法,具有广泛研究价值.     

闪电分叉过程算法优化的K-means聚类

K-means聚类算法是在数据挖掘和数据分析中一种常用算法,但是其存在依赖初始值和易陷入局部最优值的缺陷,针对这些不足,本文提出一种闪电分叉过程算法优化的K-means聚类,克服聚类算法在初始值选择困难的问题,提高K-means聚类算法的求解精度,降低陷入局部最优的可能性。从UCI数据集中选取6个真实的数据集进行仿真实验,结果表明本文改进后的聚类算法有更好的求解精度和鲁棒性。     

A NEW INVERSION METHOD OF TIME-LAPSE SEISMIC

Time-Lapse Seismic improves oil recovery ratio by dynamic reservoir monitoring. Because of the large number of seismic explorations in the process of time-lapse seismic inversion, traditional methods need plenty of inversion calculations which cost high computational works. The method is therefore inefficient. In this paper, in order to reduce the repeating computations in traditional, a new time-lapse seismic inversion method is put forward. Firstly a homotopy-regularization method is proposed for the first time inversion. Secondly, with the first time inversion results as the initial value of following model, a model of the second time inversion is rebuilt by analyzing the characters of time-lapse seismic and localized inversion method is designed by using the model. Finally, through simulation, the comparison between traditional method and the new scheme is given. Our simulation results show that the new scheme could save the algorithm computations greatly.