网络可靠性分析中自顶向下的二叉决策图构造研究

采用边界分区标识网络的思想,实现基于边界分区的自顶向下K端可靠度二叉决策图(BDD)构建算法。针对BDD构建过程中存在的节点冗余问题,提出无效边冗余消除和K点非连通冗余消除2种处理技术。在规则网络和实际工程中的实验结果表明,利用无效边冗余消除和K点非连通消除技术后的BDD改进算法,在不影响算法时间性能的情况下,可大幅缩减BDD尺度,提升K端网络可靠度分析算法性能,适用于大规模的网络可靠度分析。     

基于截断边扩展图的网络可靠度近似分析

小型网络可以快速计算出可靠度精确值,但对于大型网络,可靠性精确值的计算非常困难,因此提出一种基于截断边扩展图的网络可靠性近似分析算法。实验结果证明,该算法能够在生成较小边扩展图和等价BDD(Binary Decision Diagrams)的基础上得到误差较小的近似值。     

带路径约束的双端网络可靠性分析磁

针对带路径约束的双端网络可靠性分析问题,即一个数据包从 S 点发送到 T 点,必须经过中间若干个节点,并且经过这些节点的先后顺序具有一定约束,提出了基于 BDD 的可靠性分析算法。该算法基于边扩展图实现路径约束,即在边扩展过程中只保留符合条件的约束路径,然后构建 BDD 以及进行双端网络可靠性分析。实例分析结果验证了算法的可行性和有效性。     

基于故障树的无线传感器网络可靠度符号计算

以无线传感器网络（WSN ）中应用通信可靠性（ACR）为背景,利用故障树模型中的事件元素与逻辑门元素,建立基于故障树的WSN可靠性结构。为降低WSN可靠度计算的复杂性,给出从WSN可靠性结构转换到二元决策图BDD结构的算法,利用BDD算法优化计算过程。以分层簇型网络中可用路径以及节点冗余下的应用通信可靠性问题为例,给出其可靠性结构,利用CUDD软件包给出用递归方法实现构建基于故障树的WSN可靠性结构的BDD算法,计算以上两种情况下的WSN可靠度。实验结果表明,该方法具有可行性。     

一种新的启发式边排序策略及其性能分析

网络可靠度BDD分析方法的计算复杂度与BDD尺度线性相关,而BDD尺度严重依赖边排序质量。由于求解最优边排序是一个NP问题,在实际应用中,通常采用启发式边排序策略如BFS（Breadth First Search）和DFS（Depth First Search）。针对边排序问题,从分析基于边界集（Boundary Set）的BDD构建方法BDD BS出发,将边界集思想应用于边排序过程,提出了一种新的启发式边排序策略。性能分析和大量实验表明,新设计的边排序策略性能优于经典的DFS和BFS策略,该结果为网络可靠度BDD分析方法在大规模网络中的应用拓展了新的空间。     

边排序质量影响因素研究

网络可靠度BDD分析方法的计算性能与BDD尺度紧密相关,而BDD尺度严重依赖边排序质量。因此,边排序问题是网络可靠度BDD分析方法的重要问题。由于求解最优边排序是一个NP问题,在实际网络可靠度分析中,通常采用启发式边排序策略如BFS和DFS,它们适用不同类型的网络。然而,对于给定网络,采用何种边排序策略更优,有哪些因素影响边排序质量,迄今没有给出评判依据。利用边界集思想,提出"边界长度(BSL)"概念,并用边界长度BSL表征边排序质量,揭示边界长度BSL和BDD尺度(节点数目)之间的关系。实验结果表明,边界长度BSL与BDD尺度具有正相关性,即较小BSL对应的BDD尺度较小,较大BSL对应的BDD尺度较大,多数情况下,BSL取最值时,BDD尺度能取到(或接近)最值。这为特定网络选择(或设计)高性能边排序提供了重要的参考依据。     

基于加权网络和局部适应度的蛋白质复合物识别算法

蛋白质复合物识别对分析蛋白质网络的结构特征和模块功能具有重要意义。通常在蛋白质网络中挖掘稠密子图或模块来识别其中的蛋白质复合物,限制了其应用范围和识别的准确性。针对该问题,提出了一种基于加权网络和局部适应度的蛋白质复合物识别算法,该算法综合稠密子图的密度指标和模块性定义了新的局部适应度函数,并基于边聚集系数构建加权的蛋白质网络,根据权值选择边,在加权蛋白质网络中将种子边不断聚类扩展,从而获取具有最大综合适应度的子图作为蛋白质复合物。在酵母蛋白质等多个实际网络中试验表明,该算法能够有效提升蛋白质复合物识别的准确性。     

优先级边排序策略及其性能分析

网络可靠度BDD分析的计算复杂度与BDD尺度线性相关,而BDD尺度依赖边排序策略,边排序问题是BDD网络可靠度分析的重要问题。从网络结构特性出发,设计了优先级边排序策略并深入研究了在该策略下不同排序起点对BDD尺度的影响。实验结果表明:源点和网络中心不是高性能排序起点,最佳排序起点分布在网络边缘,网络中心点为最差排序起点。该结论可为揭示边排序影响BDD尺度的本质以及研究高效启发性边排序策略提供重要参考依据。     

基于二元决策图的网络可靠性评估

提出一种改进-二元决策图(BDD)的网络可靠性评估方法.为了解决BDD构造中有效识别同构子图的问题,将边收缩/删除法应用于BDD的图分解中,并提出了BDD的宽度优先搜索算法,通过遍历BDD图对边进行排序,为布尔函数的不交化提供了一种新的高效途径.实验结果表明,该算法具有精确性高、时间复杂度低的优点,可以避免常规最小路算...     

无圈有向设备网络可靠度仿真算法研究

在网络技术问题的研究中,3-状态设备网络系统二-终端可靠度评估的BDD算法存在着可靠度符号表达式项数多,算法效率低问题。为提高可靠性,引入串联简化和并联简化,使得BDD算法在产生分枝树的过程中遇到并联结点和串联结点就不再产生新的分枝,并且在结点存储时不存储已经保存过的结点,从而得到了3-状态设备网络系统二-终端可靠度的一个新算法。通过仿真实例表明,算法消除了冗余项、产生的分枝树节点数量大幅度减少,可一次给出3-状态设备网络系统可靠度符号表达式,算法效率显著提高。算法对复杂网络系统性能评估和系统结构设计具有重要参考意义。     

基于无效状态空间的多状态网络可靠性评估

在应用d-最小割(路)集计算多状态网络可靠度精确值算法中,运用容斥原理求解d-最小割(路)集较为复杂。为此,提出一种不需d-最小割(路)集直接计算多状态网络可靠度精确值的算法。该算法按一定规则分割状态空间,在此基础上生成无效状态空间,通过迭代计算直接获得可靠度精确值,同时通过定义边的容量下界及剩余网络。实例分析结果表明,运用该算法可减少计算量,并能精确求解d-最小割(路)集。     

基于改进的不交化最小路集的网络系统可靠性算法

本文根据不交化布尔代数及BDD原理提出了一种简化的求解不交化最小路集的改进算法。对最小路集的路长进行排序,按最小路集的不同路长分两种方法不交化:对于长度为n-1的最小路集,在保持原有弧不变外,将网络图中其余未包含在该条最小路内的弧取逆加入,直接获得不交化运算结果;其余最小路集采用BDD方法进行不交化。最后的实例计算表明,改进的算法有较小的分枝树、较高的计算效率和精度,为大型网络系统的可靠性分析提供了一种新的途径。     

考虑容量和时延约束的网络端端可靠性研究

早期对网络可靠性的研究主要是以连通性为网络功能来研究的,并没有考虑网络完成用户需求的能力。综合考虑网络容量以及时延约束,在网络部件（节点或边）的容量约束下改变网络的拓扑结构,通过改进的节点遍历法,将满足用户时延约束的有效最小路集输出,通过BDD算法对有效路集进行不交化,得出网络端端可靠度的精确值。算法采用Matlab程序实现,为评估加权网络可靠性提供了一种新方法。     

A method to evaluate the routing policy with two minimal paths within time threshold

Two attributes, the capacity and the lead time, are involved in the quickest path problem which finds a path with the minimum transmission time. The capacity of each edge is assumed to be deterministic in this problem. However, in many real-life networks such as computer, telecommunication, logistics networks, etc., each edge should be multistate due to failure, maintenance, etc. Such a network is named a multistate network. Hence, the minimum transmission time through a multistate network is not fixed. We evaluate the system reliability that a specified amount of data can be sent through a pair of minimal paths simultaneously within the time threshold. A solution procedure is first proposed to calculate it. In order to boost the system reliability, the network administrator decides the routing policy in advance to indicate the first and the second priority pairs of minimal paths. The second one will be responsible for the transmission duty if the first one fails. According to the routing policy, the system reliability can be subsequently computed. The case to transmit data through more than two minimal paths can be extended easily.     

A tool for computing computer network reliability

The computation of the reliability of a computer network is one of the important tasks of evaluating its performance. The idea of minimal paths can be used to determine the network reliability. This paper presents an algorithm for finding the minimal paths of a given network in terms of its links. Then, it presents an algorithm for calculating the reliability of the network in terms of the probabilities of success of the links of its minimal paths. The algorithm is based on a relation that uses the probabilities of the unions of the minimal paths of the network to obtain the network reliability. Also, the paper describes a tool that has been built for calculating the reliability of a given network. The tool has two main phases: the minimal paths generation phase, and the reliability computation phase. The first phase accepts the links of the network and their probabilities, then implements the first proposed algorithm to determine its minimal paths. The second phase implements the second proposed algorithm to calculate the network reliability. The results of using the tool to calculate the reliability of an example network are given.     

Transient chaotic neural network-based disjoint multipath routing for mobile ad-hoc networks

Due to mobility of wireless hosts, routing in mobile ad-hoc networks (MANETs) is a challenging task. Multipath routing is employed to provide reliable communication, load balancing, and improving quality of service of MANETs. Multiple paths are selected to be node-disjoint or link-disjoint to improve transmission reliability. However, selecting an optimal disjoint multipath set is an NP-complete problem. Neural networks are powerful tools for a wide variety of combinatorial optimization problems. In this study, a transient chaotic neural network (TCNN) is presented as multipath routing algorithm in MANETs. Each node in the network can be equipped with a neural network, and all the network nodes can be trained and used to obtain optimal or sub-optimal high reliable disjoint paths. This algorithm can find both node-disjoint and link-disjoint paths with no extra overhead. The simulation results show that the proposed method can find the high reliable disjoint path set in MANETs. In this paper, the performance of the proposed algorithm is compared to the shortest path algorithm, disjoint path set selection protocol algorithm, and Hopfield neural network (HNN)-based model. Experimental results show that the disjoint path set reliability of the proposed algorithm is up to 4.5 times more than the shortest path reliability. Also, the proposed algorithm has better performance in both reliability and the number of paths and shows up to 56% improvement in path set reliability and up to 20% improvement in the number of paths in the path set. The proposed TCNN-based algorithm also selects more reliable paths as compared to HNN-based algorithm in less number of iterations.