一种提高Ｖｉｃｓｅｋ模型收敛效率的新方法

针对Ｖｉｃｓｅｋ模型收敛速度较慢和一致程度较低的问题,利用动态网络的拓扑结构并结合复杂网络中度的概念,提出一种以度为权重提高Ｖｉｃｓｅｋ模型收敛效率的新方法．进一步以动态网络的度的幂指数得到模型的推广形式,该指数的范围为［０,∞）．仿真实验结果表明,改进后模型的收敛效率优于原模型,且收敛效率随着指数的增加而增大．

     

提高BP网络收敛速率的又一种算法

提高BP网络的训练速率是改善BP网络性能的一项重要任务。该文在误差反向传播算法(BP算法)的基础上提出了一种新的训练算法,该算法对BP网络的传统动量法进行了修改,采用动态权值调整以减少训练时间。文章提供了改进算法的仿真实例,仿真结果表明用该方法解决某些问题时,其相对于BP网络传统算法的优越性。     

提高神经网络收敛速度的一种赋初值算法研究

本文针对用于复杂非线性辨识的连续激发函数的神经网络,提出一种根据被测对象非线性特性设计网络初值的算法.算例及分析表明,这种新赋初值算法不仅能使网络的收敛速度有一定提高,辨识误差有所下降,且可避免收敛于局部极小点.     

一种提高Ad Hoc网络节点能量效率的DPM模型

在节点能量有限的Ad Hoc网络中,如何延长节点电池寿命并提高能量效率是能量问题研究的关键之处。针对这一问题,提出了移动节点的动态策略管理(DPM)模型,并在模型中引入了随机优化控制策略以提高节点能量效率。然后应用概率模型检测技术将其建模为DTMCs,并使用PRISM对这一策略和DPM中另外两种常用预测策略进行了比较,结果表明这一优化策略对网络的拥塞、延时及可靠性影响较小,其总体节能效果优于上述两种预测策略。     

一种固定时间收敛模型参考终端滑模控制方法

针对一类具有模型不确定性和外部扰动的时变非线性系统, 基于模型参考控制方法, 设计了具有固定时间收敛特性的终端滑模控制器. 首先, 提出一种带有输入饱和限幅和补偿信号滤波的模型参考控制结构; 然后针对广义误差信号, 采用新型终端滑模面设计了补偿控制器, 较好地平衡靠近和远离平衡点的收敛速度. 基于李雅普诺夫方法证明了闭环系统的稳定性和固定时间收敛特性, 并给出了收敛时间上界. 最后将该方法应用到含有极限环的非线性系统跟踪控制中, 仿真结果验证了该方法的有效性.     

有限时间快速收敛滑模变结构控制

讨论了一类非线性系统的滑模变结构控制有限时间收敛问题.提出了一种新的收敛滑模超曲面及相应控制方案.研究结果表明,系统状态变量能以较快的收敛速度在有限时间内进入各级滑模超曲面最终到达平衡点,并具有良好的动态性能.最后仿真结果验证了该方案的有效性.     

一种基于复杂网络特性的Internet拓扑模型

文章从复杂网络研究的角度出发,根据Internet的统计特征及其形成机制提出了一种基于消息传递的自组织Internet拓扑模型。该拓扑模型动态模拟整个Internet的生长过程:平面上随机分布的孤立节点通过相互发送消息,消息中保存消息源的优先度等信息,每个节点根据接收到的消息决定如何建立连接。网络由初始的孤立节点自下而上自组织形成一个具有层次结构的Internet拓扑结构。仿真试验表明由该模型生成的拓扑结构在度分布以及聚集系数等方面能够准确地吻合现实Internet拓扑结构。     

一种快速收敛的遗传算法

本文针对传统遗传算法收敛速度慢的缺点，提出了一种改进的快速收敛算法，并以八皇后问题为例进行了数值模拟，实验结果表明这种改进的遗传在收敛速度上大大优于传统遗传算法。     

一种改进的chord环构造方法

覆盖网络与物理拓扑不匹配是影响结构化P2P网络查询效率的重要因素.在构造chord环时结合节点间延时考量,使得在逻辑上相邻的节点在物理网络中也临近;并提出一种区分域内、域间的查找策略,实现了由全局搜索到区域的查询.本方法不需构造辅助网络,可实现与基础覆盖网络的有机结合.仿真结果表明,改进模型相比基本chord的平均路径长度减少约20%,查询延时减少约45%.     

On the convergence condition and convergence time of BGP

The Internet composes of thousands of Autonomous System (ASes). The Border Gateway Protocol (BGP) is the standard protocol for sharing inter-domain routing information. Unlike OSPF and IS-IS, BGP allows an AS to use a lot of attributes to express semantic rich routing policies that are consistent with its desired economic, business, performance, and security goals. However, the expressiveness could cause to delay convergence or even divergence in BGP. Recent work do not rigorously analyze the impact of the general routing policies on the convergence condition and convergence time of BGP, especially considering the widely used Multi-Exit Discriminator (MED) attribute. In this paper, we will fill this gap and give the rigorous analysis on the impact of the general routing policies on the convergence condition and convergence time of BGP, including MED attribute. We first introduce a timeless model to represent BGP with the general routing policies including the MED attribute. By incorporating the timeless model we derive a sufficient condition on these general routing policies for robust convergence of BGP. We then extend the timeless model to the real-time model by adding the edge delay. Finally, we find an upper bound on convergence time of BGP by incorporating the real-time model.     

A class of robust consensus algorithms with predefined‐time convergence under switching topologies

This paper addresses the robust consensus problem under switching topologies. Contrary to existing methods, the proposed approach provides decentralized protocols that achieve consensus for networked multiagent systems in a predefined time. Namely, the protocol design provides a tuning parameter that allows setting the convergence time of the agents to a consensus state. An appropriate Lyapunov analysis exposes the capability of the current proposal to achieve predefined‐time consensus over switching topologies despite the presence of bounded perturbations. Finally, this paper presents a comparison showing that the suggested approach subsumes existing fixed‐time consensus algorithms, which allows to provide extra degrees of freedom to obtain predefined‐time consensus protocols with improved convergence characteristics, for instance, to reduce the slack between the true convergence time and the predefined upper bound. Numerical results are given to illustrate the effectiveness and advantages of the proposed method.     

一种连续属性动态分类规则数据挖掘方法

连续属性的处理是当前分类规则中一个热点研究问题。以往的算法往往是建立在离散化过程的基础上进行的,然而,该类方法不但会破坏数据中信息的精度,同时也使得概念的转换十分困难。文章在分析了以往算法的基础上,提出了利用包含度和蕴含度的方法进行连续属性的分类规则学习,并对该种方法的属性约简问题进行了讨论。可以看出,通过该文的研究较好地解决了数据精度和动态概念挖掘的问题,利用包含度和蕴含度的方法是一个十分有价值的研究方向。     

Robust optimal design and convergence properties analysis of iterative learning control approaches

In this paper, we address four major issues in the field of iterative learning control (ILC) theory and design. The first issue is concerned with ILC design in the presence of system interval uncertainties. Targeting at time-optimal (fastest convergence) and robustness properties concurrently, we formulate the ILC design into a min-max optimization problem and provide a systematic solution for linear-type ILC consisting of the first-order and higher-order ILC schemes. Inherently relating to the first issue, the second issue is concerned with the performance evaluation of various ILC schemes. Convergence speed is one of the most important factors in ILC. A learning performance index—Q-factor—is introduced, which provides a rigorous and quantified evaluation criterion for comparing the convergence speed of various ILC schemes. We further explore a key issue: how does the system dynamics affect the learning performance. By associating the time weighted norm with the supreme norm, we disclose the dynamics impact in ILC, which can be assessed by global uniform bound and monotonicity in iteration domain. Finally we address a rather controversial issue in ILC: can the higher-order ILC outperform the lower-order ILC in terms of convergence speed and robustness? By applying the min-max design, which is robust and optimal, and conducting rigorous analysis, we reach the conclusion that the Q-factor of ILC sequences of lower-order ILC is lower than that of higher-order ILC in terms of the time-weighted norm.     

A note on the convergence of the mean shift

Mean shift is an effective iterative algorithm widely used in computer vision community. However, to our knowledge, its convergence, a key aspect of any iterative algorithm, has not been rigorously proved up to now. In this paper, by further imposing some commonly acceptable conditions, its convergence is proved.     

Almost sure exponential convergence to consensus of random gossip algorithms

In this paper, we provide a proof of almost sure exponential convergence to consensus for a general class of ergodic edge selection processes. The proof is based on the multiplicative ergodic theorem of Oseledec and also applies to continuous time gossip algorithms. An example of exponential convergence in a non ergodic case is also discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

基于效用模型的网络定价策略研究

网络定价问题一直是宽带通信网中的核心问题。在传统定价策略基础上,将服务级别引入效用模型,寻找同时满足客户效用最大化和网络提供商收益最大化的价格均衡点,在理论模型基础上,通过设定具体的用户效用函数以及网路提供商成本函数,运用仿真模拟的方法验证了这种定价策略的现实可行性。     

具有偏好关系网络结构稳定的群决策协商控制模型

挖掘群决策偏好关系结构信息,提出决策者个体偏好与群体偏好关系以及网络结构稳定的群决策协商控制模型.根据模型提供个体偏好参考基准,计算决策者及群体的偏好相容性测度.通过决策者建立、取消(断开)或加强与其他决策者的链接以及对偏好信息进行调整的策略建议,促使决策者个体偏好、群决策偏好关系网络结构正向演化,在模型框架下保证群体偏好网络结构稳定,达到群体偏好信息相容性极大的目的,为进一步信息集结提供依据.     

线性多智能体系统一致性问题的部分稳定性方法

提出了处理高阶线性多智能体系统一致性问题的线性变换.该线性变换将一致性问题转化为一个部分稳定问题.研究了一般线性协议下线性多智能体系统的三个问题: 1) 寻找一致性收敛判据; 2) 计算一致性函数; 3) 设计线性一致性协议的增益矩阵.具体来说,提出了基于矩阵 Hurwitz 稳定的一致性收敛的充分必要条件,给出了一致性函数的解析表达式,同时建立了一致性协议的增益矩阵与多智能体系统收敛时间和一致性精度的关系,并针对预先给定的收敛时间和精度要求设计了增益矩阵.