有向网络的复制机制及其相关网络分析

鉴于有向网络比无向网络包含了更多的内在信息, 而复杂网络中的基本统计量往往仅适用于无向网络, 使得有向网络的研究相对缺少, 由此提出了一个有向网络的统计量, 并分析该统计量在相关有向网络研究中的有效性. 考虑到复制是有向网络增长的一个主要动力, 定义了有向网络结点复制率和有向网络复制率的概念, 并利用结点入度分布和复制率研究了有向规则网络、复制模型网络及自然数网络. 结果显示, 完全复制模型和自然数网络的入度具有无标度特性, 其入度分布的幂律指数 都为2, 2个有向网络的复制率 , 而部分复制模型的复制率 . 因此, 有向网络的入度分布、复制率都能很好地解释完全复制模型与自然数网络的相关性, 可作为重要统计量广泛应用于有向网络研究中.     

列车编组中越“标”预警系统的逻辑设计

本文以列车编组时存在的越“标”问题为例，讨论了越“标”预警系统所需要的逻辑开关网络，并以此网络为基础，设计了整个预警电路系统，调试结果表明设计方法合理、可行.     

用人工神经网络方法研究高聚物的热力学性质

用前馈多层神经网络方法研究了高聚物的热力学性质.在本文的人工神经网络模型中,输入层为高聚物的密度ρ,缠结分子量M-e,玻璃化转变温度T-g,高聚物的高弹性模量G-N,钩状构像含量β和无扰均方末端距的特征比C-∞;输出层为高聚物比热C-p;隐含层的节点数为5.最后预测结果与实验结果非常接近,相对误差为2.2～5.4,这为研究高聚物的其它物理性质提供了一种新的方法.     

具有动态阈值的人工神经网络

在研究神经元阈值作用的基础上,提出了神经网络中神经元的动态阈值选择作用和神经元之间抑制作用模型,研究表明,这种模型能降低应用对象模型的复杂性,简化网络运行的中间过程,修正二值输出函数的性能,使收敛速度和有效性得到改善.将这种人工神经网络用于求解TSP问题,显示系统还具有模拟退火作用的效果.     

GPON体系结构研究

千兆比特无源光网络(GPON)是一种面向下一代网络(NGN)的接入技术,它具有速率高、传输距离长、效率高和可伸缩性强等关键优势.同时,GPON支持引入更高带宽的以太网新业务,并能低成本提供传统的语音服务.介绍了GPON的标准化进展,分析了GPON的系统结构和协议参考模型,并给出了传输汇聚层的分层结构.     

基于模糊神经网络的自适应PID控制器

提出了一种应用于自适应PID控制器的神经网络与模糊控制相结合的算法,该算法可以有效地解决普通PID控制器依赖于对象的数学模型的缺点,可实现控制系统的在线自适应调整,可满足实时控制的要求。仿真结果表明,基于模糊神经网络整定的PID控制器具有较好的自学习和自适应性,具有较快的响应速度。     

基于直推式学习的网络故障诊断算法

网络故障的及时诊断能够保证日常工作、学习和生活能够正常进行。传统的基于监督式学习的诊断方法依赖于大量具有鉴别意义的样本,这在实际情况中通常难以得到满足。针对上述问题,本文提出了一种基于直推式学习的诊断算法。针对大规模的网络管理的特征数据,本算法利用主成分分析对特征进行降维,并利用新的度量下的特征数据来构建拉普拉斯矩阵;该矩阵能够很好的描述带检测样本和训练样本之间的关系。在此基础上,本文设计了基于直推式学习的目标函数,并利用拉格朗日乘子法完成了优化。实验部分证明了本算法能够在有限数目的带标签的样本的前提下获得精确的分类结果,能够显著提高网络故障诊断的检测率。     

Detecting community structure using label propagation with consensus weight in complex network

Community detection is a fundamental work to analyse the structural and functional properties of complex networks.The label propagation algorithm(LPA) is a near linear time algorithm to find a good community structure. Despite various ubsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the asic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these onsensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the dge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number f partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps,y computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter o adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an pproach named the label propagation algorithm with consensus weight(LPAcw), and the experimental results showed that he LPAcw could enhance considerably both the stability and the accuracy of community partitions.     

The criteria for selecting a method for unfolding neutron spectra based on the information entropy theory

To further expand the application of an artificial neural network in the field of neutron spectrometry, the criteria for choosing between an artificial neural network and the maximum entropy method for the purpose of unfolding neutron spectra was presented. The counts of the Bonner spheres for IAEA neutron spectra were used as a database, and the artificial neural network and the maximum entropy method were used to unfold neutron spectra; the mean squares of the spectra were defined as the differences between the desired and unfolded spectra. After the information entropy of each spectrum was calculated using information entropy theory, the relationship between the mean squares of the spectra and the information entropy was acquired. Useful information from the information entropy guided the selection of unfolding methods. Due to the importance of the information entropy, the method for predicting the information entropy using the Bonner spheres' counts was established. The criteria based on the information entropy theory can be used to choose between the artificial neural network and the maximum entropy method unfolding methods. The application of an artificial neural network to unfold neutron spectra was expanded.