基于面向对象概率关系模型的威胁级别评估

针对传统贝叶斯网络对复杂对象建模存在的问题,分析了面向对象的概率关系模型。建立基于面向对象概率关系模型知识库的贝叶斯网络生成算法,建立战场威胁级别评估模型,并实现对威胁级别的评估。仿真结果证明了该方法的有效性,并且具有比贝叶斯网络模型更强的建模能力。     

对象模型、关系模型与对象关系映射技术

在软件开发过程中,对象数据和关系数据的不匹配给开发工作带来了种种负面效果,对象关系映射技术的出现很好地解决了这个难题,它使得开发人员能够使用面对对象的思维来操纵关系型数据库,从而大大提高软件开发效率。     

条件概率关系数据库模型

现实世界中大量存在着的不确定性信息，关系数据库模型仅视它们为空值，有必要增强其处理这类信息的能力，文章在总结前人工作的基础上推广关系数据库模型，创建有效处理随机型不确定性信息的条件概念关系数据库模型，该模型通过在关系模式中增加一个条件概率测度属性，为每条记录指定适当的条件概率的途径，来表示不确定性信息。文中以对象码为基本工具，创建了条件概率关系结构；以特征函数为基本工具，定义了一套基于该结构的代数运算规则。条件概率的语意比概率的语意广泛，灵活，因而该模型能有效克服概率关系模型的许多不足。     

序贯Bayes概率比检验在雷达可靠性试验中的应用

依据经典概率统计原理设计试验方案进行舰炮雷达靶场可靠性试验,试验统计分析需要较大样本量,实施难度大。利用Bayes序贯概率比检验,可以有效解决这一问题。本文从经典概率统计可靠性检验入手,详细介绍基于Bayes序贯概率比检验理论的可靠性检验原理及方案,并结合某型雷达试验实例进行分析,介绍其在雷达可靠性试验中的应用。     

序贯Bayes概率比检验在雷达可靠性试验中的应用

依据经典概率统计原理设计试验方案进行舰炮雷达靶场可靠性试验，试验统计分析需要较大样本量，实施难度大。利用Bayes序贯概率比检验，可以有效解决这一问题。本文从经典概率统计可靠性检验入手，详细介绍基于Bayes序贯概率比检验理论的可靠性检验原理及方案，并结合某型雷达试验实例进行分析，介绍其在雷达可靠性试验中的应用。     

概率数据库中元组间关系的表示

文章从介绍概率数据库的概念入手,分析了在实际应用中为了更灵活地操作关系中的元组,在原来的概率数据库基础上增加对元组之间存在的关系操作的必要性。文章提出在概率数据库中表示元组间存在的各种关系的方法,并且对这种改进进行了可行性分析。     

用Bayes网络对光纤接入网供电系统故障的监控

供电是光纤接入网中的一个重大技术问题。文中讨论了供电的两种方式,即网络供电方式和本地供电方式。对于光纤接入网,本地供电结构是一种较为理想的方案,但对本地供电网络监控则又是一个技术难点。文中设计了一种采用Ｂａｙｅｓ网络的电源监控系统网络结构,对网络进行了理论分析,并给出具体的实现方案。     

电子器件生存概率及置信度的Bayes计算方法

针对工程实践中经常遇到的小子样问题,分别以无信息分布、正态分布、产生正态性能分布作为电子器件生存概率随机变量的验前分布,给出了二项抽样实验验后生存概率密度的具体形式,根据某电子器件电流损伤阈值实验数据,得到特定辐射环境下器件生存概率的Bayes点估计、一定置信度下的双边估计、单边估计。仿真结果表明,产生正态性能验前分布要优于无信息分布和正态验前分布,该方法可以应用到工程实践中。     

IP与传输网络结合的集成模型

随着IP／MPLS技术的发展，网络控制协议功能不断强大；随着自动光网络技术的发展，传统的传送网中已经引入动态交换的概念。在这些基础上，IP与传输网络的更紧密结合成为可能。本论述了IP与传输网络结合的集成模型，并对基于GMPLS的集成模型的实现作了详细分析。     

概率图模型在无线网络推理中的应用

重点研究概率图模型发展和分类,其信号处理方法可以有效解决无线网络中一些不确定性推理问题。在无线理论研究中,讨论并分析因子图的一些已有成果,同时关注到因子图方法在无线传感网络中的应用。结合无线网络的关键技术要求对该类模型的应用领域做进一步探讨,展望未来图模型在该领域中的应用前景。     

融合关联规则与知识的贝叶斯网络学习算法

领域知识可以有效的提高贝叶斯网络学习效率与精度.文中提出了基于关联规则的SEM算法——AR-SEM算法.AR-SEM算法首先利用关联规则分析变量间的因果关系,并作为初始先验知识和领域专家的意见相结合,进一步去除无意义的规则,形成一个知识库,最后将知识库与SEM算法相结合来构造贝叶斯网络.文中在具有一定缺省数据的数据集上进行实验,实验表明AR-SEM可有效提高贝叶斯网络结构学习的精度.     

贝叶斯网络在电子产品可靠性分析中的应用

讨论了传统可靠性分析方法的优点和缺点,简述了贝叶斯网络的优点及其因果推理与诊断推理,详细讨论了桶消元法的步骤。用一个算例说明贝叶斯网络的推理过程,结果表明,贝叶斯网络的双向推理可以有效地识别电子产品的薄弱环节,为进一步提高电子产品的可靠性及提高维修效率提供依据。     

Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

This paper proposes enhancements to the channel(-state) estimation phase of a cognitive radio system. Cognitive radio devices have the ability to dynamically select their operating configurations, based on environment aspects, goals, profiles, preferences etc. The proposed method aims at evaluating the various candidate configurations that a cognitive transmitter may operate in, by associating a capability e.g., achievable bit-rate, with each of these configurations. It takes into account calculations of channel capacity provided by channel-state estimation information (CSI) and the sensed environment, and at the same time increases the certainty about the configuration evaluations by considering past experience and knowledge through the use of Bayesian networks. Results from comprehensive scenarios show the impact of our method on the behaviour of cognitive radio systems, whereas potential application and future work are identified.

Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization

A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary‐based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real‐world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.     

包含隐变量的贝叶斯网络增量学习方法

提出了一种贝叶斯网络增量学习方法——ILBN.ILBN将EM算法和遗传算法引入到了贝叶斯网络的增量学习过程中,用EM算法从不完整数据计算充分统计量的期望,用遗传算法进化贝叶斯网络的结构,在一定程度上缓解了确定性搜索算法的局部极值问题.通过定义新变异算子和扩展传统的交叉算子,ILBN能够增量学习包含隐变量的贝叶斯网络结构.最后,ILBN改进了Friedman等人的增量学习过程.实验结果表明,ILBN和Friedman等人的增量学习方法存储开销相当,但在相同条件下,学到的网络更精确;实验结果也证实了存在不完整数据和隐变量时,ILBN的增量学习能力.     

Learning Bayesian Networks with the Combination of Evolutionary Algorithm and EMI Method

Most Bayesian network （BN） learning algorithms use EMI algorithm to deal with incomplete data. But EMI algorithm is of low efficiency due to its iterative parameter refinement, and the problem will become even worse when multiple runs of EMI algorithm are needed. Besides, EMI algorithm usually converges to local maxima, which also degrades the accuracy of EMI based BN learning algorithms. In this paper, we replace EMI algorithm used in BN learning tasks with EMI method to deal with incomplete data. EMI is a very efficient method, which estimates probability distributions directly from incomplete data rather than performs iterative refinement of parameters. Base on EMI method, we propose an effec- tive algorithm, namely EMI-EA. EMI-EA algorithm uses EMI method to estimate probability distribution over local structures in BNs, and evaluates BN structures with a variant of MDL scoring function. To avoid getting into local maxima of the search process, EMI-EA evolves BN struc- tures with an Evolutionary algorithm （EA）. The experi- mental results on Alarm, Asia and an examplar network show that EMI-EA algorithm outperforms EMI-EA for all samples and E-TPDA algorithms for small and middle size of samples in terms of accuracy. In terms of efficiency, EMI-EA is comparable with E-TPDA algorithm and much more efficient than EMI-EA algorithm. EMI-EA also out- performs EMI-EA and M-V algorithm when learning BNs with hidden variables.     

基于贝叶斯网络的客户流失分析

随着电信市场竞争加剧,客户流失现象成为电信运营商关注的问题.文中基于数据挖掘手段,采用贝叶斯网络分类器,进行电信客户流失分析.在贝叶斯网络构造过程中,结合采用K2和MC-MC算法构建网络.根据贝叶斯网络的拓扑结构,筛选出客户流失相关的显著指标;由条件概率表确定客户的流失规则,进而确定高流失的客户群.考虑分类的误判损失函数,给出不同分类临界值下,贝叶斯网络模型的分类效果.与其它分类算法相比,比如决策树和人工神经网络,在客户流失率很低的情况下,该算法不需要进行＂过量抽样＂.     

Analytical Models for Single-Hop and Multi-Hop Ad Hoc Networks

There is considerable interest in modeling the performance of ad hoc networks analytically. This paper presents approximate analytical models for the throughput performance of single-hop and multi-hop ad hoc networks. The inherent complexity of analysis of a multi-hop ad hoc network together with the fact that the behavior of a node is dependent not only on its neighbors' behavior, but also on the behavior of other unseen nodes makes multi-hop network analysis extremely difficult. However, our approach in this paper to analyze multi-hop networks offers an accurate approximation with moderate complexity. Our approach is based on characterizing the behavior of a node by its state and the state of the channel it sees. This approach is used to carry out an analysis of single-hop and multi-hop ad hoc networks in which different nodes may have different traffic loads. In order to validate the model, it is applied to IEEE 802.11-based networks, and it is shown through extensive simulations that the model is very accurate. Farshid Alizadeh-Shabdiz received his B.Sc. in 1989 at University of Science and Technology, M.Sc. in 1991 at Tehran University, Iran, and D.Sc. in 2004 at the George Washington University. He is a senior research engineer in Advanced Solution Group, part of Cross Country Automotive Services, and he is also a part time faculty member at Boston University. Dr. Alizadeh-Shabdiz was part of the design and implementation team of the three first satellite-based mobile networks: ICO global medium orbit satellite network voice and data services, Thuraya GEO satellite network, and the first phase of Inmarsat high speed data network. His research interests include MAC layer, physical layer and network layer of wireless and satellite networks. Suresh Subramaniam received the Ph.D. degree in electrical engineering from the University of Washington, Seattle, in 1997. He is an Associate Professor in the Department of Electrical and Computer Engineering at the George Washington University, Washington, DC. His research interests are in the architectural, algorithmic, and performance aspects of communication networks, with particular emphasis on optical and wireless ad hoc networks. Dr. Subramaniam is a co-editor of the books “Optical WDM Networks – Principles and Practice” and “Emerging Optical Network Technologies: Architectures, Protocols, and Performance”. He has been on the program committees of several conferences including Infocom, ICC, Globecom, and Broadnets, and served as TPC Co-Chair for the 2004 Broadband Optical Networking Symposium. He currently serves on the editorial boards of Journal of Communications and Networks and IEEE Communications Surveys and Tutorials. He is a co-recipient of the Best Paper Award at the 1997 SPIE Conference on All-Optical Communication Systems.     

贝叶斯网络在大规模医疗数据上的应用研究

针对医院信息系统中积累的大量数据,探讨了采用粗糙集、规则推理、贝叶斯网络相结合的方法基于这类数据进行学习建模.该方法在粗糙集属性约简的基础上,考虑了规则推理的影响,对信息表中的属性列进行压缩,获取最少属性列.基于最少属性的贝叶斯网络模型可以有效降低网络结构的复杂性;同时利用贝叶斯网络实现概率推理.最后进行了实验分析,结果证明该方法快速有效.     

概率图模型的表示理论综述

概率图模型结合概率论与图论的知识,利用图结构表示变量的联合概率分布,近年已成为不确定性推理的研究热点.随着概率图模型在实际领域中的应用日益增加,不同的任务和应用环境对概率图模型的表示理论提出了不同的新要求.本文总结出近年来提出的多种概率图模型的表示理论.最后指出概率图模型的进一步研究方向.