用改进蚁群算法求解无线传感器网络多sink节点关联问题

在无线传感器网络W SN(wireless sensor networks)中使用多个sink节点既能有效减少传感器节点与sink之间的距离,又能有效降低通信中的能量消耗。如何为传感器节点分配sink节点使得系统总能耗最低,称为多sink节点的关联问题。首先建立带约束的多sink节点关联问题的优化模型,进而用蚂蚁算法解决给定多sink节点部署方案下的普通节点与sink节点间的关联问题,最后给出相关算法的仿真结果。     

基于最大模式的关联规则挖掘算法研究

提出了一种基于最大模式的关联规则挖掘算法,探讨了它的实现步骤,最后通过实例说明它是数据挖掘中一种有效的关联规则挖掘算法。     

一种改进的频繁模式挖掘算法

本文在分析研究FP-growth算法的基础上,提出了一种基于传统事务数据库下的频繁模式挖掘改进算法。实验证明该算法比FP-growth算法更有效,并具有较好的扩展性。     

改进的频繁模式挖掘算法

FP-growth算法是关联规则挖掘中一种经典的算法,它不需要产生候选集,只需要扫描事务数据库两次来构建项目头表和FP-Tree.但该算法项节点查询比较耗时,而且要递归生成条件FP-tree,所以内存开销大.针对上述问题,文中提出了一种基于FP-growth的新的频繁模式挖掘算法MGFP-growth.其思想是:首先算法弃用项目头表,使用二维矩阵存储事务的信息,按照矩阵列进行分组,并建立parenttrace关系;最后利用存储在数组中的gourp信息可以快速的构建频繁模式树,从而进行频繁项集的挖掘.实验表明,该算法只对事务数据库扫描一次,同时利用分组将项存储,节省了内存空间,有效解决了传统算法的固有缺陷,提高了算法效率.     

关联维数的并行求解算法

关联维数的求解是分形理论中的一个重要问题,标准算法由于其巨大的计算量,不能满足实时任务的需要,过去的改造算法集中在串行地减少求解多个关联维数时的重复计算量,并未从根本上降低O(N^2)次的向量距离计算、距离比较和求和次数,其应用范围和性能改善程度是有限的。本文给出了两个并行算法：基于PRAM模型的花费O(N^2／p logp)时间p个处理机的算法,和基于LARPBS模型的花费O(N^2p)时间p个处理机的算法。相对纯理论的PRAM算法,LARPBS算法是实际可行的,它是目前时间复杂度最低的算法,并且是最优可扩展和成长最优的。     

一种改进的关联分类算法

关联分类算法是数据挖掘技术中一种主要分类方法,但传统关联分类算法仅根据置信度构造分类器,影响分类精度。提出一种改进算法,在选择高置信度构造分类器的基础上,优先考虑短规则分类。实验结果表明,该改进算法在分类精度和分类器大小上均优于传统分类算法。     

基于线索频繁模式树的关联规则产生算法

虽然FP-Growth算法能够有效地从数据库中挖掘频繁模式,但如何由其挖掘出的频繁模式中高效地产生关联规则仍是一个相当复杂的问题。该文提出了用于组织频繁模式的线索频繁模式树(TFPT)和一个从TFPT中挖掘关联规则的高效算法—最短模式优先算法(SPF)。挖掘模式Y的关联规则时,SPF算法应用了两个优化策略,避免了对大量的不可能成为规则XY-X左部的Y的子集的检查,从而获得了很好的性能。实验表明:与类FP-Growth算法结合时,SPF算法运行速度远远快于Apriori算法,并有相当好的可伸缩性。     

面向异常检测的关联模式快速挖掘算法

随着网络和其它信息技术的广泛应用,网络数据流量急剧增长,但现有网络流量异常监测的准确性与实时性均达不到实际应用的需求,迫切需要对流量数据进行快速、深层次的分析.因此,提出一种快速关联模式挖掘算法,通过提取重要的网络数据特征进行关联挖掘,不仅为流量数据分析判断提供及时准确的参考和借鉴,而且提高了监测准确性和效率.     

基于模式相关的改进BP神经网络算法

针对传统的BP神经网络模式分类算法在各个网络输出值较为接近或者模式类之间的网络输出值接近的情况下容易发生误判的问题,提出一种基于模式相关的BP神经网络分类算法,并结合具体电路,运用该方法进行建模、仿真．实验结果表明,采用模式相关的BP神经网络分类算法能够充分利用网络输出层各个节点的所有输出,增强了网络的输出特性,便于正确、方便的进行模式分类,且分类效果良好,具有一定的通用性．     

复杂信息处理中因果关联模型的构建及主因判定算法

近来,诸如图形图像、音频视频等复杂类型信息的分析和处理,已日益受到各学科领域专家和学者的重视,文章首先介绍了复杂信息处理中因果关联模型的构建,然后给出了主因分析方法的构造和实现过程,并提出了具体的算法和实例检验。     

Rollout strategy-based probabilistic causal model approach for the multiple fault diagnosis

Multiple fault diagnosis (MFD) is used as an effective measure to tackle the problems of real-shop floor environment for reducing the total lifetime maintenance cost of the system. It is a well-known computationally complex problem, where computational complexity increases exponentially as the number of faults increases. Thus, warrants the application of heuristic techniques or AI-based optimization tools to diagnose the exact faults in real time. In this research, rollout strategy-based probabilistic causal model (RSPCM) has been proposed to solve graph-based multiple fault diagnosis problems. Rollout strategy is a single-step iterative process, implemented in this research to improve the efficiency and robustness of probabilistic causal model. In RSPCM instead of finding all possible combinations of faults, collect the faults corresponding to each observed manifestations that can give the best possible result in compared to other methods. Intensive computational experiments on well-known data sets witness the superiority of the proposed heuristic over earlier approaches existing in the literature. From experimental results it can easily inferred that proposed methodology can diagnosed the exact fault in the minimum fault isolation time as compared to other approaches.     

Grammatical analysis of Hindi sentences using connectionist approach

Abstract: This paper presents a simple connectionist approach to parsing of a subset of sentences in the Hindi language, using Rule based Connectionist Networks (RBCN) as suggested by Fu in 1993. The basic grammar rules representing Kernel Hindi sentences have been used to determine the initial topology of the RBCN. The RBCN is based on a multilayer perceptron, trained using the backpropagation algorithm. The terminal symbols defined in the language structure are mapped onto the input nodes, the non-terminals onto hidden nodes and the start symbol onto the single output node of the network structure. The training instances are sentences of arbitrary, but fixed maximum length and fixed word order. A neural network based recognizer is used to perform grammaticality determination and parse tree generation of a given sentence. The network is exposed to both positive and negative training instances, derived from a simple context-free-grammar (CFG), during the training phase. The trained network recognizes seen sentences (sentences present in the training set) with 98–100% accuracy. Since a neural net based recognizer is trainable in nature, it can be trained to recognize any other CFG, simply by changing the training set. This results in reducing programming effort involved in parser development, as compared to that of the conventional AI approach. The parsing time is also reduced to a great extent as compared to that of a conventional parser, as a result of the inherent parallelism exhibited by neural net architecture.     

基于因果网络模型的飞机通信系统故障诊断

研究了基于因果网络模型的故障诊断方法,首先建立因果网络模型,并进行模型匹配;然后用Warshall算法实现邻接矩阵到可达矩阵的转换,采用逻辑数组法产生候选诊断模型;接着利用概率论的方法给出系统故障诊断的推理策略;最后,将该诊断模型应用于飞机通信系统,诊断实例验证了设计方法的实用性.     

一种改进的基于免疫网络模型(aiNet) 的故障诊断算法

根据抗体群与抗原群的匹配关系,提出一种改进的基于免疫网络模型(aiNet)的故障诊断算法.建立了自适应调整剪枝和抑制阈值的规则,并对K近邻算法的附加距离阈值加以限制,提高了基于aiNet故障诊断算法对已知故障的识别率,克服了其不能识别新故障的缺点.仿真结果表明,改进算法具有优良的故障诊断性能.     

基于FTA和比较模型的网络故障诊断算法

针对网络故障特点,将MM*模型和FTA方法引入网络故障诊断建模中,设计了一种用FTA方法进行网络故障的系统分析与诊断,并定量求解出所有故障可能的最小割集,然后用MM*模型从最小割集中选取测试点对进行单点故障检测与定位的网络故障诊断算法。实验结果表明,该算法有效提高了网络故障诊断的效率和准确率,具有较好的实用性。     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

基于PMC故障模型的NASD诊断算法

在PMC故障模型下,现有的自适应顺序诊断算法（ASD算法）不能充分利用所有的测试结果。为了有效地减少测试次数,提高诊断效率,提出一种新的自适应顺序诊断算法（NASD算法）。引入相对故障单元的概念,给出并证明了故障单元和无故障单元的判别定理。据此给出系统诊断的策略：（1）边寻求无故障单元边确诊故障单元;（2）已确认的故障单元不再参与任何测试;（3）找到无故障单元或故障单元数接近一半时,系统诊断结束。实例表明,NASD算法优于其他ASD算法。     

基于Chwa & Hakimi故障模型的二分诊断算法

在对Chwa &; Hakimi故障模型的诊断中,目前相对成熟的算法有t-可诊断性算法和方程诊断算法两大类。然而,上述两类算法各有其优缺：前者要求故障处理机的数目小于处理机总数的一半;后者则希望故障处理机的数目多多亦善。不仅指出何时采用t-可诊断性算法或方程诊断算法,而且建立了所谓的二分诊断算法,即当故障处理机数量占处理机总数一半左右时将原测试系统拆分为两部分：相对正常机集合和相对故障机集合,从而对各个处理机集合采用各自适合的算法去诊断。     

基于推荐信任模型改进拜占庭容错共识算法

针对拜占庭容错算法存在通信开销大、节点选取简单、对恶意节点缺乏惩罚机制的问题,提出了一种基于推荐信任模型的改进拜占庭容错共识算法。引入P2P网络下的推荐信任模型,根据节点在共识阶段的行为,计算各节点的全局信任值,使用节点选取机制,解决节点选取简单的问题。全局信任值高的节点进入共识组,恶意节点被踢出共识组不再参与共识,解决恶意节点缺乏惩罚机制的问题。实验表明,R-PBFT较PBFT具有更低的网络开销和更高的容错性。