动态连续时间网络表示学习

随着时间的推移,网络会随着节点和连边的变化不断发展。针对传统网络表示学习算法不能正确处理动态网络的问题,提出一种基于随机游走的动态连续时间网络表示学习算法（DCTNE）。通过定义一个灵活的节点时序邻居概念,设计一个有偏的随机游走过程。根据时间信息,有效地探索节点的不同时序邻居并建模不同邻居的影响,学习网络表示。实验证明了DCTNE动态网络时序信息的有效性。在链接预测任务上,DCTNE的AUC值与其他算法相比最高获得了50%的增益;在节点分类任务上,DCTNE相较于其他算法在效果上有明显提升。结果表明,对网络中时间依赖关系进行建模有助于后续的网络分析任务。     

连续时间容量网络的最短动态时间流问题

针对一类带节点处理速率限制的连续时间容量网络,提出了该网络中的最短动态时间流问题,并给出其线性规划形式;通过分析该网络与经典网络之间的内在联系,利用最大接收流和退流的思想分别设计出准确求解两种网络最短动态时间流的高效算法;证明了算法的正确性并分析出算法有较小的复杂度;最后,通过一个算例演示了算法的执行。     

离散时间最优控制—–评论动态规划

阐述离散时间最优控制的特点.对比3种求解离散时间最优控制的解法,即:1)用非线性规划求解离散时间最优控制;2)用无约束优化求解离散时间最优控制;3)动态规划及其数值解.1)和2)都适用于多维静态优化,计算效率较高,是高级方法.在名义上,3)为动态优化.实际上,3)为一维分段无约束静态优化,计算效率较低,是初级方法.本文并用数字实例进一步阐明动态规划及其数值解在求解方面较差,故动态规划及其数值解已失去实用价值.在求解离散时间最优控制问题方面,无法与非线性规划求解相匹敌.     

一类随机连续信号的时间序列分析与建模

讨论由随机微分方程描述的随机连续信号的辨识建模问题,提出并证明了非平稳的连续Ｗｉｅｎｅｒ过程通过稳定的连续线性系统后为平稳随机过程,且均值和自相关函数阵为时间遍历的,基于状态空间分析,给出了连续随机信号建模的时间序列分析方法,并证明了参数估计的一致性收敛性,仿真结果显示了所提出方法的有效性,     

基于学习连续时间事件序列的动态网络链路预测

动态网络链路预测是目前复杂网络的热点研究方向,网络表示学习可以有效学习到节点的相似性,从而为链路预测提供基础.现有的动态网络表示学习方法大多先将动态网络进行离散窗口化,然后在静态网络快照图上建模,这样很难有效处理具有细粒度时间特性的动态网络.本文提出了一种可以学习动态网络中复杂的时间特性的链路预测模型,该模型使用连续时间事件序列表示动态网络,对网络中的连续时间信息和结构演化特征进行学习,并提出了基于时间注意力的信息传递机制来模拟网络中信息的扩散与聚合,最后将链路预测转化为分类问题.实验在4个真实动态网络数据集以及模拟网络上进行,并以ap和auc作为评价指标.真实网络实验结果证明该模型能够较好地学习网络演化的连续性,得到更有效的节点表示,从而提升了链路预测效果.模拟网络的实验结果表明链路预测的效果和网络模型相关,但本文模型仍可以获得较好的预测效果.     

基于GFS-Net的动态复制

GFS-Net是一种新的DHT覆盖网络。本文介绍了GFS-Net的拓扑结构,并完善了其搜索算法,提出了与GFS-Net相适应的动态复制算法,它包括两个部分：（1）根据文件的请求频率增减副本数目;（2）根据下裁性能较低的站点的分布确定新增副本的位置。该算法可以根据副本的请求频率和分布,动态调整副本的数目和分布,平衡各个站点的负载,降低用户下裁文件的响应时间。     

连续Markov跳变系统最优控制

针对连续Markov跳变系统,对其最优控制问题进行研究.首先,基于随机最大值原理,设计完全状态信息情形下Markov跳变系统的最优控制器;然后,采用导数原始定义结合Markov跳变系统特性的方法,得到了最优控制器系数矩阵的黎卡提微分方程,进而将其推广到不完全状态信息情形;最后,通过数值仿真验证了所得控制器的正确性.     

网格计算中动态复制策略的设计

网格计算是新型的分布计算技术,是解决复杂度越来越高的新应用的重要方法。该文简述了网格计算的定义、特点和基本结构。着重讨论了其中大数据量的全局共享和广泛分布问题。介绍的基于网格环境的动态复制技术,可以减少带宽消耗,扩大存储潜力。对3种不同存储模式的6种复制策略的特性作了评价。     

动态非线性连续时间系统的小波神经网络辨识

将小波神经网络应用于动态非线性连续时间系统的辨识, 同时为了使神经网络的训练达到全局最优和加速小波神经网络训练的收敛速度, 提出了信赖域算法, 并研究了信赖域算法的收敛性. 随后进行了算例仿真, 证明了所提辨识方法的有效性.     

Threshold-Based Dynamic Replication in Large-Scale Video-on-Demand Systems

Recent advances in high speed networking technologies and video compression techniques have made Video-on-Demand (VOD) services feasible. A large-scale VOD system imposes a large demand on I/O bandwidth and storage resources, and therefore, parallel disks are typically used for providing VOD service. Although striping of movie data across a large number of disks can balance the utilization among these disks, such a striping technique can exhibit additional complexity, for instance, in data management, such as synchronization among disks during data delivery, as well as in supporting fault tolerant behavior. Therefore, it is more practical to limit the extent of data striping, for example, by arranging the disks in groups (or nodes) and then allowing intra-group (or intra-node) data striping only. With multiple striping groups, however, we may need to assign a movie to multiple nodes so as to satisfy the total demand of requests for that movie. Such an approach gives rise to several design issues, including: (1) what is the right number of copies of each movie we need so as to satisfy the demand and at the same time not waste storage capacity, (2) how to assign these movies to different nodes in the system, and (3) what are efficient approaches to altering the number of copies of each movie (and their placement) when the need for that arises. In this paper, we study an approach to dynamically reconfiguring the VOD system so as to alter the number of copies of each movie maintained on the server as the access demand for these movies fluctuates. We propose various approaches to addressing the above stated issues, which result in a VOD design that is adaptive to the changes in data access patterns. Performance evaluation is carried out to quantify the costs and the performance gains of these techniques.     

脉冲控制扩展定理及应用

本文在现有脉冲控制理论的基础上, 针对离散时滞系统, 提出了一种扩展脉冲控制的数学描述方法.基于该描述方法, 推导出脉冲控制扩展定理.该扩展定理的合理应用不仅可以有效避免执行器饱和特性的影响, 而且可以分析执行器存在响应时间时系统的稳定性.进一步研究发现, 当系统存在Neimark-Sacker分岔时, 依据扩展定理设计的控制器可以有效提高系统的临界分岔参数.     

Deterministic and stochastic control of discrete-time bilinear systems

Optimal control of a class of time invariant single-input, discrete bilinear systems is investigated in this paper. Both deterministic and stochastic problems are considered.

In the deterministic problem, for the initial state in a certain set ∑₀, the solution is the same as the solution to the associated linear system. The optimal path may be a regular path or a singular path.

The stochastic control problem is considered with perfect state observation, and additive and multiplicative noise in the state equation. It is demonstrated that the presence of noise simplifies the analysis compared to that in the determinstic case.     

Dynamic output feedback control synthesis for stochastic time-delay systems

This article deals with the dynamic output feedback control synthesis problem for Itô-type stochastic time-delay systems. Our aim is to design a full order dynamic output feedback controller to achieve the desired control objectives. We will formulate the controller design problem as an H _∞ optimisation problem in the mean-square sense. The main contributions of this article are as follows: (i) for stochastic systems, the design of a controller with multiple objectives can be addressed without employing a unique Lyapunov function; (ii) using an inequality technique and Finsler Lemma, we provide convex controller synthesis conditions described by linear matrix inequalities (LMIs). Some examples are presented to show the effectiveness of the developed theoretical results.     

Adaptive Linear Quadratic Regulator for Continuous-Time Systems With Uncertain Dynamics

In this paper, adaptive linear quadratic regulator (LQR) is proposed for continuous-time systems with uncertain dynamics. The dynamic state-feedback controller uses input-output data along the system trajectory to continuously adapt and converge to the optimal controller. The result differs from previous results in that the adaptive optimal controller is designed without the knowledge of the system dynamics and an initial stabilizing policy. Further, the controller is updated continuously using input-output data, as opposed to the commonly used switched/intermittent updates which can potentially lead to stability issues. An online state derivative estimator facilitates the design of a model-free controller. Gradient-based update laws are developed for online estimation of the optimal gain. Uniform exponential stability of the closed-loop system is established using the Lyapunov-based analysis, and a simulation example is provided to validate the theoretical contribution.      

随机时延网络化控制系统的动态增益调度策略*

研究了一种通过网络中间件对原有控制器的输出进行外部增益调整的方法,以实现不改变已存在控制器的前提下,动态补偿随机网络时延对控制系统的影响。利用离散二次型最优规划算法设计了二次型最优增益参数序列,建立了与随机时延序列相对应的二次型最优增益参数序列查询表,并介绍了利用查表法进行在线调整增益策略的步骤。通过仿真实例验证了这种外部增益调整策略可以有效地补偿随机时延对控制系统的影响。     

Optimal control of stochastic dynamic systems with past history and poison switchings

An optimal control problem for systems of stochastic differential-functional linear equations with past history and Poisson switchings is formulated. The Bellman equation is solved for this problem.Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 112–118, November–December 2004.This revised version was published online in April 2005 with a corrected cover date.     

一类新的动态车辆调度问题的建模与算法

针对目前对于动态车辆调度问题的研究仅集中于考虑时间依赖或依概率变化的情形,在对原有动态车辆调度问题模型进行总结的基础上,综合考虑了时间依赖且网络依概率变化,以及结合带有时间窗和随机需求的情况,提出了新的问题模型,并提出求解该问题模型的多目标随机机会约束规划模型,设计了用遗传算法解决该模型的方案与步骤。实验结果表明,所提出的模型可有效地拟合交通状况,设计的算法可以有效地求解该模型。     

动态扩张算法的一个性质*

动态扩张算法在控制系统的动态反馈综合问题以及结构分析问题上都有着重要的应用。本文证明动态扩张算法的一个有趣的性质:由动态扩张算法所得到的扩张系统,不依赖于我们怎样施行该算法。本文还指出这个结论与几个非线性控制系统的分析和设计问题的联系。