Heuristics for Two-Level Hierarchical Transportation Networks

Given a set of points, we wish to design a network consisting of a primary link and a set of secondary links connecting the points to the primary link. The objective of the problem is to find the location and length of the primary link in order to minimize the sum of its weighted length and the weighted lengths of all secondary links. We assume that the weight of the secondary link from any point varies depending on the location of that point. In this paper, we describe efficient algorithms and their computer implementation for two scenarios of this problem. In the first scenario, only direct secondary links are allowed from each point to the primary link. In the second scenario, the secondary link from a point is allowed to pass through other points before reaching the primary link.     

二维有序样本的有约束系统聚类

二维有序样本进行聚类必须满足两个要求：（１）类内各单元的相似性和类间的差异性；（２）各单元在位置上的有序性和类内的连通性。根据这些要求，将各单元观测指标间的距离矩阵作为聚类的指示矩将各单元之间的区位联系矩阵作为聚类的约束矩阵，在约束矩阵给出的约束条件之下，以类间单元指标的最大距离作为类间相似性指标，在指示矩阵中通过逐步聚并而将全部单元合并归类，即可得出满足要求的样本分类。     

Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

Previous research has resulted in a number of different algorithms for rule discovery. Two approaches discussed here, the ‘all-rules’ algorithm and multi-objective metaheuristics, both result in the production of a large number of partial classification rules, or ‘nuggets’, for describing different subsets of the records in the class of interest. This paper describes the application of a number of different clustering algorithms to these rules, in order to identify similar rules and to better understand the data.     

基于分层序列的移动电子商务推荐系统策略研究

移动互联网环境下,移动电子商务快速发展,移动推荐系统成为解决移动信息过载的主要技术和手段.移动电子商务推荐系统中用户策略选择是一个典型的多目标决策问题,目标之间也存在一定的矛盾.基于移动推荐系统的这种特征,借助分层序列法算法的思想,通过元数据的标准化处理,结合移动电子商务中的位置上下文信息获取推荐数据,得到满足用户多目标需求的推荐结果.方法的提出为移动电子商务推荐系统的研究提出了一种新的推荐策略.     

基于多层Bayes估计的战略协同网络供应链可靠性的参数估计

在战略协同网络中供应链的可靠性研究中,引进失效信息,对可靠性参数进行了估计,并给出了实证,结果表明本文给出的方法可行.     

一种动态获取簇核心的自动聚类tNN-MEANS算法

K-means算法是一种非常重要的聚类算法,然而算法的聚类效果受簇的个数、初始中心点位置的影响很大.提出基于优化初始中心集合和中心移动算法tNN-MEANS,算法有效解决了以下三个问题:1)准确确定大规模数据集中簇的个数;2)精确确定全局高密度的核心区域;3)克服了簇中存在多个高密度区域的问题.运用UCI数据集分别对X-means算法、DBSCAN算法和tNN-MEANS算法进行对比实验,实验结果验证了tNN-MEANS算法的聚类精度、确定簇的个数、蔟划分的正确率等性能均优于与之对比的其它算法.     

基于分级诊疗体系的转诊与政府投资决策研究

针对我国分级诊疗实施过程中,由于基层医疗机构服务能力不足所造成的“上转容易下转难”的困境,研究了政府部门对社区医院服务能力进行投资以促进病人下转的问题.通过构建四阶段的博弈模型,探讨了为实现医疗系统总病人效用最大化的政府资金均衡投入策略,为实现三甲医院期望效益最大化的病人下转策略,病人效用最大化下的社区医院服务能力规划以及基于期望效益最大化下的病人选择.研究发现：1)三甲医院的期望效益受到政府投资金额的影响,当政府投资金额不足时,三甲医院的期望效益仅能达到次优;2)三甲医院病人的均衡转诊率受到三甲医院的治疗价格和政府的资金投入量的影响;3)在实现整个医疗系统总病人效用最大化上,虽然总病人效用随政府投入金额的增加而增加,但从政府角度来讲,政府投资金额不宜过大.     

A Markov Renewal Based Model for Wireless Networks

We propose a queueing network model which can be used for the integration of the mobility and teletraffic aspects that are characteristic of wireless networks. In the general case, the model is an open network of infinite server queues where customers arrive according to a non-homogeneous Poisson process. The movement of a customer in the network is described by a Markov renewal process. Moreover, customers have attributes, such as a teletraffic state, that are driven by continuous time Markov chains and, therefore, change as they move through the network. We investigate the transient and limit number of customers in disjoint sets of nodes and attributes. These turn out to be independent Poisson random variables. We also calculate the covariances of the number of customers in two sets of nodes and attributes at different time epochs. Moreover, we conclude that the arrival process per attribute to a node is the sum of independent Poisson cluster processes and derive its univariate probability generating function. In addition, the arrival process to an outside node of the network is a non-homogeneous Poisson process. We illustrate the applications of the queueing network model and the results derived in a particular wireless network.     

约束非线性规划的神经网络算法

神经网络具有内在大规模并行运算和快速收敛特性,它在最优化技术上的运用近年来受到广泛的重视。本提出一个新的求解一般约束非线性规划的神经网络模型,它具有全局收敛性和广泛的适用性,是求解一般非线性规划问题的新工具。理论分析和模拟计算均表明了模型的有效性。     

关于模糊控制中一类模糊推理方法的研究

本文以模糊控制中一类最常见的模糊推理形式为例，深入探讨了模糊推理中规则与事实相匹配的问题，分别就语言变量和语义提出了优化匹配原则，以选择规则库中必要的信息对既有事实进行快速推理。     

基于样本最大分类信息的聚类有效性函数

根据每一样本的最大分类信息,对Bezdek定义的划分系数进行修改,提出了一个新的聚类有效性函数,讨论了该函数的若干性质。实验结果表明在某些情形该函数的判决效果优于划分系数。     

Some Models for a Hierarchical Manpower System

This paper is an extension of previous work which described a model for a hierarchical manpower system with two grades. The model is first extended to a system with several grades, and then to a system with several types of entrant or in which the type of entrant has changed over time. Finally, a calculation is described which can be used to determine the best mix of qualifications amongst the entrants.     

一种基于模糊聚类的区间值属性约简算法

针对区间值信息系统基于粗糙集理论提出一种新的属性约简算法:首先计算同一属性下对象间的相似度,然后通过合取算子计算出所有属性下对象之间的相似度矩阵,再用模糊聚类中的传递闭包算子得到等价矩阵,将区间值信息系统转化为具有等价关系的信息系统并且进行约简,从而得到λ-核,同时给出了该算法的复杂度.最后通过一个实例表明这种算法的有效性和合理性.     

基于聚类分析的二手车保值率预测

二手车的保值率是指二手车销售价格与新车销售价格之比,可为未来二手车的交易和回购提供基准价格,但是二手车保值率的确定应该建立在大量的二手车历史交易数据的基础上,对于上市时间不长或成交数据不多的二手车,则很难断定其保值率.针对部分车型的二手车保值率预测中缺乏历史数据的难题,应用分层聚类的方法将15种车型保值率分为5类,筛选保值率相似车型,获得预测所需数据,依此构建该类车型的多元回归预测模型.根据实际数据检验,证明方法是可行的,且精度较高.     

A Recommender System based on Idiotypic Artificial Immune Networks

The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen–antibody interaction for matching and idiotypic antibody–antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.Mathematics Subject Classifications (2000) 68Rxx, 68Txx, 90Bxx.     

Tensor Networks and Hierarchical Tensors for the Solution of High-Dimensional Partial Differential Equations

Hierarchical tensors can be regarded as a generalisation, preserving many crucial features, of the singular value decomposition to higher-order tensors. For a given tensor product space, a recursive decomposition of the set of coordinates into a dimension tree gives a hierarchy of nested subspaces and corresponding nested bases. The dimensions of these subspaces yield a notion of multilinear rank. This rank tuple, as well as quasi-optimal low-rank approximations by rank truncation, can be obtained by a hierarchical singular value decomposition. For fixed multilinear ranks, the storage and operation complexity of these hierarchical representations scale only linearly in the order of the tensor. As in the matrix case, the set of hierarchical tensors of a given multilinear rank is not a convex set, but forms an open smooth manifold. A number of techniques for the computation of hierarchical low-rank approximations have been developed, including local optimisation techniques on Riemannian manifolds as well as truncated iteration methods, which can be applied for solving high-dimensional partial differential equations. This article gives a survey of these developments. We also discuss applications to problems in uncertainty quantification, to the solution of the electronic Schrödinger equation in the strongly correlated regime, and to the computation of metastable states in molecular dynamics.     

复杂网络幂律度分布和层次聚集函数标度指数的一种新的估计方法

提出一种估计复杂网络幂律度分布和层次聚集函数标度指数的新方法,并给出求解这些指数的数值算法．该方法可以克服目前网络研究中采用的图形线性拟合估计方法存在的偏差和不准确等不足之处．此外,通过对10个CNN网络进行KS检验统计量的比较,证实该方法比图形方法具有更好的拟合效果．     

基于系统动力学的产业集群对企业绩效的影响机制分析

以系统动力学为基础,研究了产业集群对企业绩效的影响机制。分别建立了外部资源环境、发展规模及科研创新能力对企业绩效的影响系统因果图,在此基础上开展了产业集群对企业绩效影响的全过程动力分析。以上海市医药制造产业集群为例开展了实证研究,使用Vensim模拟软件进行了模拟仿真,详尽探讨了影响机制中正负反馈回路。结果表明,产业集群的外部资源环境、产业集群的发展规模和产业集群的科研创新能力对集群内企业的发展有着正向影响,政府和集群内企业可以通过加大对集群科研实力的投资力度、改善产业集群外部环境、扩大产业集群发展规模三个方面来提高产业集群内的企业绩效,促进产业集群内企业的可持续性发展。