闭排队网络当性能函数与参数相关时的性能灵敏度分析

借助于无穷小矩阵摄动方法, 讨论了一类Markov过程, 其稳态性能关于参数摄动的灵敏度分析问题. 然后研究了闭排队网络的稳态性能灵敏度分析问题, 并在参数相关性能函数的情况下, 给出了网络的几种稳态性能的灵敏度公式. 这些公式表明稳态性能灵敏度很容易通过网络势能进行计算.     

状态相关闭排队网络中的性能指标灵敏度公式

本文通过研究一类Ｍａｒｋｏｖ过程在无穷小矩阵的参数摄动下稳态性能指标的灵敏度,运用无穷小矩阵的群逆,实现矩阵和势能这三个描述稳定性能指标灵敏度的等价量,给出了状态相关闭排队网络在参数摄动下的稳态性能指标灵敏度公式,这些结果可直接用于排队网络的控制和优化。     

限定记忆的前向神经网络在线学习算法研究

从理论上分析了隐含层激励函数满足Mercer条件的前向神经网络的数学本质,给出了网络学习的指导方向.提出3种网络在线学习算法,它们通过动态调整网络结构和权值来提高网络在线预测性能.算法完全符合统计学习理论提出的结构风险最小化原则,具有较快的学习收敛速度和良好的抗噪声能力.最后通过具体数值实验验证了上述算法的可行性和优越性.     

Potential-based online policy iteration algorithms for Markov decision processes

Performance potentials play a crucial role in performance sensitivity analysis and policy iteration of Markov decision processes. The potentials can be estimated on a single sample path of a Markov process. In this paper, we propose two potential-based online policy iteration algorithms for performance optimization of Markov systems. The algorithms are based on online estimation of potentials and stochastic approximation. We prove that with these two algorithms the optimal policy can be attained after a finite number of iterations. A simulation example is given to illustrate the main ideas and the convergence rates of the algorithms.     

Computational Functionalism for the Deep Learning Era

Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism.     

通过功率控制建立密度自适应的分簇无线传感器网络

无线传感器网络的分簇密度是不定的,通过控制簇头发射功率将能使分簇密度控制在合理范围内.无线传感器网络的"簇头--成员"结构与CDMA的"基站--移动台"结构相似,可以采用CDMA的功率控制机制对无线传感器网络的最小ID分簇算法进行优化.改进后的算法和步骤得到了描述,并给出了控制簇头发射功率的流程图.     

计算无线通信网络2-终点可靠性的快速算法

提出计算无线通信网络从源点到汇点(ST)可靠性的一个计算公式.比直接用容斥原理计算包含更少的项和更少的算术运算.提出一个计算从网络源点到汇点(ST)可靠性算法.这个算法本质上是通过不交和运算生成一些新网络来计算其ST可靠性或生成可靠性表达式.所得到的新网络数量结构简单,容易计算.最后通过一个具体例子说明了这个结论.     

Dual and selfdual networks of queues with rejection blocking

Open and closed queueing networks with rejection blocking are investigated. Using the concept of holes, a duality is derived for pairs of networks. This duality equates equilibrium state probabilities and throughputs of the given network and its dual. As an application an exact product form solution for certain closed queueing networks with rejection blocking are derived. In this case, duality provides a simple method to compute performance measures. In some cases, a network and its dual have the same structure. Duality provides relations among performance measures of different stations in this case.     

双总线结构工业控制计算机网性能指标评估

本文对双总线结构工业控制计算机网给出了其Ｐｅｔｒｉ网和高级Ｐｅｔｒｉ网的建模及性能指标评估方法。文中考虑了信包缓冲区容量有限、信包最大允许服务时间有限、不同的站点优先级和系统总线故障率等因素影响下的网络性能指标评估问题。克服了以往对网络性能评估的某些局限。本文讨论了带计数禁止弧的高级随机Ｐｅｔｒｉ网（ＨＤＳＰＮ）的特性，并对给出的ＤＳＰＮ模型进行了仿真计算，对影响网络性能的系统指标进行了讨论。     

移动Ad hoc网络中基于稳定性的QoS路由算法综述

简要回顾了移动Ad hoc网络的基本概况,分析了在移动Ad hoc网络中提供QoS保障的重要性。简单介绍了移动Ad hoc网络中路由层QoS保障研究的现状和进展。在此基础上,系统地介绍了基于稳定性的QoS路由算法的各组成部分,并对其进行了细致的分类和科学的性能比较。最后,给出了研究方向。     

On the impact of soft handoff in cellular systems

We present a model for soft handoff in wireless cellular networks. In such networks, due to overlapping cells, handoffs are not instantaneous and multiple channels may be occupied by a single mobile for a non-zero freeze time period.We provide a mathematical model of wireless cellular networks with soft handoffs. We examine different performance measures and show that freeze time may have a major impact on the system performance if the mobility rate is not negligible. Both exact and approximate formulations are given. Different fixed-point approximation methods are used to reduce the complexity of the exact solution. Various performance measures such as new and handoff blocking and probability of a call dropout are carefully examined.     

移动Ad Hoc网络中带路由机制的密钥管理

移动Ad Hoc网络是一种无中心自组织的多跳无线网络,有效的密钥管理是网络安全的关键。因此,本文提出了一个能够快速响应的密钥管理框架。这个框架通过应用可验证的秘密共享方案来建立PKI,并通过引入路由机制构造了一个快速响应的服务器组。服务器组可以对组内结点的私钥分量更新和所有结点的证书更新提供快速响应。为了验证框架的
的性能,本文通过仿真给出了实验结果。     

Correlations and population dynamics in cortical networks

The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.     

波分复用光传输网中波长路由算法的研究进展

光纤正迅速成为主干通信网的标准传介媒质．随着光学器件的发展，使得信号在传输过程中，除了在源、汇节点需要光电转换外，中间节点可保持光传输，这种通信网络叫光传送网．光传送网中的波分复用技术是将整个光纤的带宽分成多个信道，不同的信道可使用不同的波长来同时进行信息传输，从而增加了整个网络的带宽．在光传送网中，实现一个通信请求需要建立一条通信路径，并为该通信路径所经过的每条链上分配一个波长，即所谓波长路由．该文详细介绍了波分复用光传送网中波长路由算法的研究进展，内容包括波长分配算法、网络的信元阻塞率分析、容错和QoS波长路由、多播波长路由、最小化ADM数路由以及基于光或光电连接的并行机模型等．     

延迟离散Hopfield网络的动态特征分析

神经网络的稳定性被认为是神经网络各种应用的基础．主要利用网络的状态转移方程和能量函数来研究带有延迟项的离散Hopfield神经网络动力学行为．给出了延迟离散Hopfield神经网络收敛于周期小于等于2的极限环的一些充分条件．给出了延迟网络收敛于周期为2和4的特殊极限环的一些充分条件．同时，得到了网络不存在任何稳定点的一些必要条件．所获结果不仅推广了一些已有的结论，而且为网络的应用提供了一定的理论基础．     

Performance analysis of CSMA and BTMA protocols in multihop networks (I). Single channel case

Busy tone multiple access protocols have been used in multihop networks to reduce the effect of the hidden terminal problem. Due to complexity, the performance of these protocols for large networks has not been analyzed. In this paper, using a Markov chain model and an approximation, we are able to analyze and evaluate the throughput performance of the non-persistent CSMA protocol, the conservative busy tone multiple access (C-BTMA) protocol and the ideal destination-based busy tone multiple access (ID-BTMA) protocol for large networks. The throughput comparison of the protocols is given. The results show that in a large multihop network, the BTMA protocols have a better performance than the non-persistent CSMA protocol and the ID-BTMA protocol has a better performance than the C-BTMA protocol at light channel loads.     

Efficient computation based on stochastic spikes

The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.     

前向傅立叶神经网络系统逼近理论及学习算法

定义了傅立叶神经元与傅立叶神经网络,将一组傅立叶基三角函数作为神经网络各隐层单元的激合函数,设计出一类单输入单输出三层前向傅立叶神经网络与双输入单输出四层前向傅立叶神经网络,以及奇、偶傅立叶神经网络,基于三角函数逼近论,讨论了前向傅立叶神经网络的三角插值机理及系统逼近理论,且有严格的数学理论基础,给出了前向傅立叶神经网络学习算法,通过学习,它们分别能逼近于给定的傅立叶函数到预定的精度。仿真实验表明,该学习算法效率高,具有极为重要的理论价值和应用背景。     

Markov 控制过程在紧致行动集上的迭代优化算法

研究一类连续时间Markov控制过程(CTMCP)在紧致行动集上关于平均代价性能准则的优化算法。根据CTMCP的性能势公式和平均代价最优性方程，导出了求解最优或次最优平稳控制策略的策略迭代算法和数值迭代算法，在无需假设迭代算子是sp—压缩的条件下，给出了这两种算法的收敛性证明。最后通过分析一个受控排队网络的例子说明了这种方法的优越性。