北海航道班轮运输排班模型的构建与求解

首先,以拟开通的大连港至鹿特丹港北海航道班轮航线为研究对象,以油耗和运营成本最小化为优化目标,将官方已有记载的北海航道通过的货船航速数据输入Eureqa软件,得到航行最高速度f(x)与进入北海航道时间t的拟合度最好的函数公式.在此基础上,构建了北海航道班轮运输排班的数学模型;其次,在仿真实例分析中,选用了5000TEU的集装箱船作为计算参数,将捕食搜索算法运用到数学模型求解之中,并且运用MATLAB软件进行求解,在最快航速受限的前提下,得到单艘班轮最佳往返时间和相同型号班轮的最低配置数目以及在服务周期内班轮公司所要付出的最低运营成本;最后,通过比较研究,得出结论是:最快航速的提高,对于降低北海航线班轮运输的运营成本将起较大作用.     

基于RBM的神经网络时间序列预测

为了克服神经网络依赖初始化结果,泛化能力不强的缺点,提出一种基于受限玻尔兹曼机(RBM)的神经网络模型.利用无监督学习方法优化神经网络的初始权值和阈值,将RBM与神经网络融合起来,模型与时间序列神经网络做实验对比,结果表明,基于受限的玻尔兹曼机的神经网络模型优于神经网络预测模型,模型可以提高预测的精准度,具有一定的应用意义.     

优化MGM(1,n)模型及其应用研究

从最小二乘估计的适用条件出发,在建立MGM(1,n)模型前,先采用BoxCox变换对原始数据进行变换,以解决或缓解误差项的正态性偏离问题.首先,以模型拟合平均误差最小化为目标,建立非线性优化模型,在参数的置信区间内,应用PSO(Particle Swarm Optimization)算法求最优解.然后,应用求解得到的参数,建立优化MGM(1,n)模型.在实例分析中,将优化MGM(1,n)模型应用于深基坑围护结构变形预测,实验结果表明通过对原始数据做合适的Box-Cox变换,能够有效提高模型的拟合及预测精度,拓广多变量灰色预测模型的适用范围.     

含中介值ξ-η-ζ等式的证明方法

介绍含三个中介值ξ-η-ζ等式的几种证明方法,并结合实例加以说明.     

时间表问题与遗传算法

通过对时间表问题和遗传算法的认识,探讨用遗传算法求解时间表问题,并给出一个实例。     

Optimal Conditional Estimation: Average Case Setting

We consider an estimation problem which appears in the identification of systems by means of restricted complexity models: find the optimal approximation to an element of a linear normed space (a system) based on noisy information, subject to the restriction that approximations (models) can be selected from a prescribed subspace M of the problem element space. In contrast to the worst-case optimization criterion, which may be pessimistic, in this paper the quality of an identification algorithm is measured by its local average performance. Two types of local average errors are considered: for a given information (measurement) y and for a given unknown element x, the latter in two versions. For a wide spectrum of norms in the measurement space, we define an optimal algorithm and give expressions for its average errors which show the dependence on information, information errors, unmodelled dynamics, and norm in the measurement space.     

AN ASYMPTOTICAL O((k + 1)n~3L) AFFINE SCALING ALGORITHM FOR THE P_*(k)-MATRIX LINEAR COMPLEMENTRITY PROBLEM

1. IlltroductionAn LCP is normally for finding vectors x, s E Wu such that:where q E aam and M E Rrsxn. An LCP is called monotonic if M is positive semi-definite. Inthis paperg M is assumed to be a P*(K)--matrir[6][9] i.e. for a K 2 0, M satisfies:for any u E ac. Obviouslys positive semi-definite matrix is a p*(0)-matrix. It wajs proved in[10] that M is a P*(K)--matrix if f M is a sufficient[1].Based on Dikin's approach, Monteiro and Adler proposed in [81 an affine scaling algorith…     

On solving the maximum clique problem

The Maximum Clique Problem (MCP) is regarded here as the maximization of an indefinite quadratic form over the canonical simplex. For solving MCP an algorithm based upon Global Optimality Conditions (GOC) is applied. Furthermore, each step of the algorithm is analytically investigated and tested. The computational results for the proposed algorithm are compared with other Global Search approaches.     

Spherical Minimax Location Problem

This paper presents an algorithm for determining a minimax location to service demand points that are equally weighted and distributed over a sphere. The norm under consideration is geodesic. The algorithm presented here is based on enumeration and has a polynomial time complexity.     

建立模糊诊断矩阵的一种新方法

本文首先提出了一个新的模糊逻辑算子——模糊加权综合算子“#”,进而给出建立模糊诊断矩阵的新方法——网络法 ,并给出了相应的算法