由Markov网到Bayesian网

Markov网（马尔可夫网）是类似于Bayesian网（贝叶斯网）的另一种进行不确定性揄的有力工具，Markov网是一个无向图，而Bayesian网是一个有向无环图，发现Markov网不需要发现边的方向，因此要比发现Bayesian网容易得多，提出了一种通过发现Markov网得到等价的Bayesian网的方法，首先利用信息论中验证信息独立的一个重要结论，提出了一个基于依赖分析的边删除算法发现Markov网，该算法需O（n^2)次CI（条件独立）测试，CI测试的时间复杂度取决于由样本数据得到的联合概率函数表的大小，经证明，假如由样本数据得到的联合概率函数严格为正，则该算法发现的Markov网一定是样本的最小L图，由发现Markov网，根据表示的联合概率函数相等，得到与其等价的Bayesian网。     

基于遗传算法的Bayesian网中连续变量离散化的研究

文中如何从含有离散变量和连续变量的混合数据中学习Bayesian网进行了研究，提出了一种基于遗传算法的连续变量散化算法，在该处中给出了兼顾离散模型准确度和复杂度的适应度函数；并基于对离散化的实质性分析，定义了离散策略等价的概念，由此制定了离散策略的编码方案；进一步设计了变换离散策略的遗传算法。算法不存在局部极值问题，且不需要事先给定变量序关系，模拟实验结果表明，该算法能有效地对连续变量散化，从而使得从混合数据中学到的Bayesian网具有较好性能。     

贝叶斯网等价类学习算法

贝叶斯网用一种紧凑的形式表示联合概率分布,具有完备的语义和坚实的理论基础,目前已成为人工智能领域处理不确定性问题的最佳方法之一。贝叶斯网学习是其关键问题,传统学习方法存在如下不足：（1）随节点数增多非法结构以指数级增加,影响学习效率;（2）在等价结构之间进行打分搜索,影响收敛速度;（3）假设每个结构具有相同的先验概率,造成等价类中包含结构越多则先验概率越高。本文提出一种学习马尔科夫等价类算法,该算法基于骨架空间进行状态转换,利用从骨架空间到等价类空间的映 映射关系实现学习贝叶斯网等价类。实验数据证明,该方法可有效缩小搜索空间规模,相对于在有向图空间搜索的算法加快了算法的收敛速度,提高了执行效率。     

基于遗传算法的Bayesian网结构增量学习的研究

已建成的Bayesian网与领域环境间可能存在较大偏差,加之领域本身固有的动态变化特性,因此在观察到新数据时,改善Bayesian网的性能和优化网络结构是十分必要的．提出了一种基于遗传算法的Bayesian网（包含结构和参数）求精算法．该算法基于上次的求精结果把已有的不完备数据转化成完备数据,以期望充分统计因子作为已有数据的主要存储形式,基于本次求精过程中的当前最佳个体对新数据进行完备化,并由遗传操作综合利用新数据和已有数据进行求精．模拟实验结果表明,该增量学习算法能较有效地从不完备数据中求精Bayesian网．     

基于遗传算法的Bayesian网结构学习研究

从不完备数据中学习网络结构是Bayesian网学习的难点之一,计算复杂度高,实现困难。针对该问题提出了一种进化算法。设计了结合数学期望的适应度函数,该函数利用进化过程中的最好Bayesian网把不完备数据转换成完备数据,从而大大简化了学习的复杂度,并保证算法能够向好的结构不断进化。此外,给出了网络结构的编码方案,设计了相应的遗传算子,使得该算法能够收敛到全局最优的Bayesian网结构。模拟实验结果表明,该算法能有效地从不完备数据中学习。     

基于粒子群优化算法的Bayesian网络结构学习

近年来,Bayesian网络已经成为人工智能领域的研究热点.为了更广泛的应用Bayesian网络,本文采用粒子群优化搜索算法,通过对粒子群算法中各个算子的确定,从训练数据样本中学习到Bayesian网络结构,并用测试数据样本测试学习结果与训练数据的匹配程度,试验结果表明,该算法能有效地学习到Bayesian网络结构.     

Bayesian网知识推理在ITS学习推荐中的应用研究

提出了一种基于Bayesian网的知识推理网络和知识推理算法,该算法利用Bayesian知识推理网和Bayesian概率公式,从现有学习资源库和教学方法库中推荐出最符合学生特征的k种学习资源和k种教学方法,从而实现ITS的智能学习推荐功能。     

贝叶斯网结构学习搜索空间分析

贝叶斯网结构学习是一个NP难题,提高学习效率是重要研究问题之一。贝叶斯网结构空间的规模随节点(随机变量)数呈指数增加,选择适当的结构空间可以提高学习效率。本文对贝叶斯网结构空间进行定性和定量分析,对比有向图空间、贝叶斯网空间和马尔科夫等价类空间的规模和特点。通过实验数据分析先验结构空间约束对降低结构空间规模的效率,给出约束参数的选择区间。为贝叶斯网结构学习选择搜索空间和确定约束参数提供理论支持,从而提高学习效率。     

线性离散时滞系统的Gl2状态反馈控制

研究具有状态时滞的线性离散时不变系统的Gl2控制问题．基于简化的Gl2分析定理,Gl2控制问题被等价转化为H∞控制问题．通过LMI和构造Lyapunov函数,得到了Gl2性能的LMI表述．给出了使得时滞系统稳定且满足给定性能指标的无记忆Gl2状态反馈控制器存在的充分条件和设计方法．当系统具有独立范数有界扰动输入和全结构不确定性时,使用此方法设计得到的Gl2控制器比H∞控制器的保守性更小．算例验证了该方法的有效性.     

神经网络BP学习算法动力学分析

研究神经网络BP学习算法与微分动力系统的关系.指出BP学习算法的迭代式与相 应的微分动力系统数值解Euler方法在一定条件下等价,且二者在解的渐近性方面是一致的. 给出了神经网络BP学习算法与相应的微分动力系统解的存在性、唯一性定理和微分动力系统 的零解稳定性定理.从理论上证明了神经网络的学习在一定条件下与微分动力系统的数值方法 所得的数值解在渐近意义下是等价的,从而借助于微分动力系统的数值方法可以解决神经网络 的学习问题.最后给出了用改进Euler方法训练BP网的例子.     

基于GATS贝叶斯网络结构学习的航班延误模型

对遗传算法（GA）贝叶斯网络（BN）结构学习和禁忌搜索算法（TS）进行分析,提出遗传禁忌搜索贝叶斯网络结构学习算法GATS_BNSL。把禁忌搜索思想引入到遗传算法BN结构学习由父代种群产生后代种群的演化过程中,以禁忌搜索交叉和禁忌搜索变异改进传统的遗传算子,对比实验分析表明了GATS_BNSL的学习优势。应用此方法,基于真实数据,建立了大型枢纽机场航班离港延误模型。该模型切实反映了导致航班延误的多因素之间的因果关系,而且建模时间少,学习正确率高。     

构建贝叶斯网络本质图的新方法

等价类学习是贝叶斯网络结构学习的一个重要分支,而本质图是贝叶斯网络等价类的图形表示,是进行等价类学习的有力工具。针对求解贝叶斯网络结构本质图存在的繁琐问题,提出了一种构建贝叶斯网络本质图的组合算法。该算法从初始非循环有向图开始,对所有有向边进行排序,保持V-结构中的边不变,将不参与V-结构的有向边转化为无向边,依次根据三条规则判定各条无向边在本质图中的方向。给出了算法的理论证明,通过具体案例分析验证了算法的有效性。     

On the use of Bayesian Networks to develop behaviours for mobile robots

Bayesian Networks are models which capture uncertainties in terms of probabilities that can be used to perform reasoning under uncertainty. This paper presents an attempt to use Bayesian Networks as a learning technique to manage task execution in mobile robotics. To learn the Bayesian Network structure from data, the K2 structural learning algorithm is used, combined with three different net evaluation metrics. The experiment led to a new hybrid multiclassifying system resulting from the combination of 1-NN with the Bayesian Network, that allows one to use the power of the Bayesian Network while avoiding the computational burden of the reasoning mechanism — the so-called evidence propagation process. As an application example we present an approach of the presented paradigm to implement a door-crossing behaviour in a mobile robot using only sonar readings, in an environment with smooth walls and doors. Both the performance of the learning mechanism and the experiments run in the real robot-environment system show that Bayesian Networks are valuable learning mechanisms, able to deal with the uncertainty and variability inherent to such systems.     

贝叶斯网络结构在线学习算法研究及应用

现代大型机电设备的日趋复杂化和自动化导致设备故障现象和机理之间具有很大的不确定性,因此对故障诊断技术提出了更高的要求。针对汽车发动机的工作原理及其故障知识结构特征,基于贝叶斯网络理论,以机器学习中的增量学习为基础提出和研究了在线式贝叶斯网络结构学习方法,并利用该方法对汽车发动机故障结构网络进行在线学习。最后通过实验分析验证了在线式贝叶斯网络故障诊断方法比起传统的贝叶斯网络方法以及专家系统方法,该方法在汽车发动机故障诊断结果中具有更高的准确性和可靠性。     

基于预测关系的贝叶斯网络学习算法

在介绍有代表性的贝叶斯网络结构学习算法基础上,给出了变量之间预测能力的概念及估计方法,并证明了预测能力就是预测正确率,在此基础上建立了基于变量之间预测关系的贝叶斯网络结构学习方法,并使用模拟数据进行了对比实验,实验结果显示该算法能够有效地进行贝叶斯网络结构学习。     

Exploring gene causal interactions using an enhanced constraint-based method

DNA microarray provides a powerful basis for analysis of gene expression. Bayesian networks, which are based on directed acyclic graphs (DAGs) and can provide models of causal influence, have been investigated for gene regulatory networks. The difficulty with this technique is that learning the Bayesian network structure is an NP-hard problem, as the number of DAGs is superexponential in the number of genes, and an exhaustive search is intractable. In this paper, we propose an enhanced constraint-based approach for causal structure learning. We integrate with graphical Gaussian modeling and use its independence graph as an input of our constraint-based causal learning method. We also present graphical decomposition techniques to further improve the performance. Our enhanced method makes it feasible to explore causal interactions among genes interactively. We have tested our methodology using two microarray data sets. The results show that the technique is both effective and efficient in exploring causal structures from microarray data.     

Bayesian Networks for Data Mining

A Bayesian network is a graphical model that encodesprobabilistic relationships among variables of interest. When used inconjunction with statistical techniques, the graphical model hasseveral advantages for data modeling. One, because the model encodesdependencies among all variables, it readily handles situations wheresome data entries are missing. Two, a Bayesian network can be used tolearn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequencesof intervention. Three, because the model has both a causal andprobabilistic semantics, it is an ideal representation for combiningprior knowledge (which often comes in causal form) and data. Four,Bayesian statistical methods in conjunction with Bayesian networksoffer an efficient and principled approach for avoiding theoverfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarizeBayesian statistical methods for using data to improve these models.With regard to the latter task, we describe methods for learning boththe parameters and structure of a Bayesian network, includingtechniques for learning with incomplete data. In addition, we relateBayesian-network methods for learning to techniques for supervised andunsupervised learning. We illustrate the graphical-modeling approachusing a real-world case study.     

Application of Bayesian Network Learning Methods to Waste Water Treatment Plants

Bayesian Networks have been proposed as an alternative to rule-based systems in domains with uncertainty. Applications in monitoring and control can benefit from this form of knowledge representation. Following the work of Chong and Walley, we explore the possibilities of Bayesian Networks in the Waste Water Treatment Plants (WWTP) monitoring and control domain. We show the advantages of modelling knowledge in such a domain by means of Bayesian networks, put forth new methods for knowledge acquisition, describe their applications to a real waste water treatment plant and comment on the results. We also show how a Bayesian Network learning environment was used in the process and which characteristics of data in the domain suggested new ways of representing knowledge in network form but with uncertainty representations formalisms other than probability. The results of applying a possibilistic extension of current learning methods are also shown and compared.     

Bayesian Multi‐net Classifier for classification of remote sensing data

The purpose of this paper is to investigate the applicability of Bayesian Multi‐net Classifier (BMC) to classify remote sensing data. BMC is based on Bayesian Network (BN), which is a graphical model encoding probabilistic relationships among variables of interest. Different from the BNC that has a mere network, a BMC has as many local Bayesian Networks as the predefined classes, which means that the probabilistic relationships among the features can be different for different classes. Classification is done by computing the probability of the class, given the particular instance of the features, and then predicting the class with the highest posterior probability. This method was validated using a Landsat ETM+ image of Beijing acquired on 1 May 2003. Based on the confusion matrix, overall accuracy, Kappa statistic, total normalized probability of misclassification (TNPM), and McNemar's test, classification results of BMC were compared with those of MLC and BNC in the case study. The comparison results show that BMC performs slightly better than MLC and similar to BNC. The local Bayesian Networks of BMC can also lead to a better understanding of the dependencies between bands for different classes.     

一种改进的软件项目开发风险管理模型

该文提出了一种改进的软件项目开发风险管理模型。该模型在贝叶斯网络的建模过程中以样本数据集为基础进行结构学习和参数学习,建立更符合实际软件项目特征的贝叶斯网络。同时,进一步完善了软件项目开发风险管理流程,并利用贝叶斯网络的信念更新过程实现动态软件项目风险管理。经实践检验,该改进模型能够更有效地对软件项目开发过程中的风险进行管理,提高软件开发的成功率。