Partition consistency

This paper provides a case study of specifying an abstract memory consistency model, providing possible implementations for the model, and proving the correctness of implementations. Specifically, we introduce a class of memory consistency models called partition consistency. Existing abstract consistency models such as sequential consistency, piplined-RAM, Goodman’s processor consistency, and coherence are all members of the partition consistency class. A concrete message-passing network model is also specified. Implementations of partition consistency on this network model are then presented and proved correct. A middle level of abstraction is utilized to facilitate the proofs. All three levels of abstraction are specified using the same framework. The paper aims to illustrate a general methodology and techniques for specifying memory consistency models and proving the correctness of their implementations.     

The self-trapping attractor neural network-part II: properties of a sparsely connected model storing multiple memories

In a previous paper, the self-trapping network (STN) was introduced as more biologically realistic than attractor neural networks (ANNs) based on the Ising model. This paper extends the previous analysis of a one-dimensional (1-D) STN storing a single memory to a model that stores multiple memories and that possesses generalized sparse connectivity. The energy, Lyapunov function, and partition function derived for the 1-D model are generalized to the case of an attractor network with only near-neighbor synapses, coupled to a system that computes memory overlaps. Simulations reveal that 1) the STN dramatically reduces intra-ANN connectivity without severly affecting the size of basins of attraction, with fast self-trapping able to sustain attractors even in the absence of intra-ANN synapses; 2) the basins of attraction can be controlled by a single free parameter, providing natural attention-like effects; 3) the same parameter determines the memory capacity of the network, and the latter is much less dependent than a standard ANN on the noise level of the system; 4) the STN serves as a useful memory for some correlated memory patterns for which the standard ANN totally fails; 5) the STN can store a large number of sparse patterns; and 6) a Monte Carlo procedure, a competitive neural network, and binary neurons with thresholds can be used to induce self-trapping.     

A self‐organizing recurrent fuzzy CMAC model for dynamic system identification

This paper presents a self‐organizing recurrent fuzzy cerebellar model articulation controller (RFCMAC) model for identifying a dynamic system. The recurrent network is embedded in the self‐organizing RFCMAC by adding feedback connections with a receptive field cell to the RFCMAC, where the feedback units act as memory elements. A nonconstant differentiable Gaussian basis function is used to model the hypercube structure and the fuzzy weight. An online learning algorithm is proposed for the automatic construction of the proposed model during the learning procedure. The self‐constructing input space partition is based on the degree measure to appropriately determine various distributions of the input training data. A gradient descent learning algorithm is used to adjust the free parameters. The advantages of the proposed RFCMAC model are summarized as (1) it requires much lower memory requirement than other models; (2) it selects the memory structure parameters automatically; and (3) it has better identification performance than other recurrent networks. © 2008 Wiley Periodicals, Inc.     

基于信任关系的无线自组网分簇方法

针对传统的分簇方法很少考虑安全因素,或者只考虑安全性而忽视对网络性能影响的问题,提出一种基于信任关系的分簇方法。该方法结合人类记忆的扩散激发模型的思想,能够根据有限的局部信息,自动地对整个网络进行分割,在提高AdHoc网络性能的同时,还可提高其安全性。实验结果表明,该分簇方法在精确度方面与集中式的分簇方法非常接近。     

RBF neural networks for the prediction of building interference effects

Wind loads on tall buildings can be quite different from those on an isolated building due to neighboring building effects. With the increase of number of tall buildings in large cities, there is a growing attention to the interference effects among adjacent buildings under wind action. While wind tunnel tests are of importance in the understanding of the physical process, the general quantitative predictions of interference effects are difficult to reach owing to many variables involved. In the present paper, a radial basis function (RBF) neural network is proposed for its strong ability in nonlinear mapping and its higher training speed. Thus the RBF neural network is applied to evaluate the interference effects (expressed by interference factor, IF) by using experimental data obtained from many sources as training patterns. The results indicate that a very good agreement is found between the predicted IF values and the experimental counterparts.     

A neural network model of memory under stress

A model that attempts to simulate animal memory under stress is presented. For this purpose a model of selectable multiple associative memories is given. We consider two underlying types of memories: stressed and unstressed, implemented on the same neural network. In our model, learning into one or the other type of memory is done according to the stress of the individual at the time of learning. Memory retrieval is obtained according to a continuous function of the stress of the individual at the time of retrieval, who for low stress retrieves unstressed associations and for high stress retrieves stressed associations. Several biological results supporting this model are presented. A mathematical proof on the behaviour of the basins of attraction of the network as a function of stress is presented. Also a generalization to selectable multiple coexisting memories is given, and engineering and other applications of the model are suggested.     

基于振荡型混沌神经网络的智能信息处理研究

提出了振荡型混沌神经网络的结构模型和动力方程,证明了该网络的稳定性和Hopf 分支的必要条件,得到了在三维模式空间中的轨迹,利用功率谱密度分析了重迭函数的波形.试 验结果表明输入模式在靠近和远离记忆模式两种情况时,网络具有不同的信息处理能力.该网 络在时空模式的动力信息处理和智能信息处理系统中有很重要的应用价值.     

An algorithm for architecture partitioning in high-speed networks with bursty traffic

This paper analyzes the performance of a switching architecture. The performance measures include the elapsed time of packet transfer and the waiting time to begin transfer. The architecture is partitioned depending on the type of network used and the expected traffic in the network. Every partition has a switch with a buffer that can absorb surges of bursty traffic within the network partition. The buffer size depends on the type of the network and incoming traffic. The partition size depends on the network bandwidth, network traffic, packet size and buffer size. Examples of different networks are used to show the applications of the model. The results show that the elapsed time of packet switch transfer depends exponentially on the number of partitions in the network.     

并行编译中数据分布的线性划分模式计算

由于计算机访问本地存储器的速度远远快于通过网络访问异地计算机存储器的速度，因此，在分布式存储环境中，如何对程序中引用的数据进行合理的分布，从而达到在本地进行计算时只需访问存储在本地的数据（即无通信的数据分布）的目的，已成为提高并行计算速度的关键问题，本文主要讨论如何在数组下标表达式为线性的条件下，对一种种锘于线性代数中超平面概念的数组线性划分技术进行扩充，并给出了完整的数据划式计算算法。     

基于无线传感器网络的土壤水分监测基站系统设计

根据土壤水分监测传感器网络应用的需求,提出一种基于无线传感器网络的土壤水分监测基站系统的设计方案,包括监测网络体系结构、硬件结构及软件功能模块分割的总体方案设计。此外,还对基站无线通信模块、数据转换模块、存储模块、人机界面和数据上传模块进行详细设计。在实验测试中,本系统运行状况良好、工作稳定,基本能满足实际应用的需要。     

A Study of a Mechanism for Controlling Multiprogrammed Memory in an Interactive System

This paper deals with the following mechanism for controlling the multiprogramming set in a demand paging system: processes are dynamically divided into several categories according to the number of page faults generated during their residence in main memory. A process is admitted into the multiprogramming set only if there is enough space free in the main memory to contain the number of pages corresponding to the current category of the process. Using a queueing network model of an interactive system with such a control mechanism we study the effectivenesss of the control considered, and, more particularly, its ability to partition the memory space according to the locality of processes.     

A comprehensive performance evaluation of crossbar networks

A comprehensive model for evaluating crossbar networks in which the memory bandwidth and processor acceptance probability are primary measures considered is presented. This analytical model includes all important network control policies, such as the bus arbitration and rejected request handling policies, as well as the home memory concept. Computer simulation validates the correctness of the model. It is confirmed that the home memory and dynamic bus arbitration policy improve the network performance     

Improving the effect of fuzzy clustering on RBF network’s performance in terms of particle swarm optimization

This paper proposes a novel training algorithm for radial basis function neural networks based on fuzzy clustering and particle swarm optimization. So far, fuzzy clustering has proven to be a very efficient tool in designing such kind of networks. The motivation of the current work is to quantify the exact effect of fuzzy cluster analysis on the network’s performance and use it in order to substantially improve this performance. There are two key theoretical findings resulting from the present work. First, it is analytically proved that when the standard fuzzy c-means algorithm is used to generate the input space fuzzy partition, the main effect this partition imposes to the network’s square error (i.e. performance index) can be written down in terms of a distortion function that measures the ability of the partition to recreate the original data. Second, using the aforementioned distortion function, an upper bound of the network’s square error can be constructed. Then, the particle swarm optimization (PSO) is put in place to minimize the above upper bound and determine the network’s parameters. To further improve the accuracy, the basis function widths and the connection weights are fine-tuned by employing a steepest descent approach. The main experimental findings are: (a) the implementation of the PSO obtains a significant reduction of the square error while exhibiting a smooth dynamic behavior, (b) although the steepest descent further decreases the error it finally obtains smaller reduction rates, meaning that the strongest impact on the error reduction is provided by the PSO, and (c) the improved performance of the proposed network is demonstrated through an extensive comparison with other related methods using a 10-fold cross-validation analysis.     

基于Spark的大规模网络结构发现算法

当今社会处于大数据时代,现实中的网络数据越来越多,其结构复杂、规模庞大,有效分析其结构对了解、应用其提供的信息具有重要作用。基于混合模型的网络结构发现算法可挖掘网络中的多类型聚类结构,但不能有效处理大规模网络。基于Graph X图计算模型,提出基于Spark的大规模网络的结构发现算法LNSES,从存储空间和运行时间两方面提升算法效率。为减少网络结构发现算法存储大规模网络邻接矩阵内存耗费量,LNSES算法将边、节点及节点静态属性值进行分布式存储,边分区记录节点连边,可作为索引进行节点间参数传递。为提高网络结构发现算法效率,边分区和节点分区进行拉链操作产生索引结构;更新参数时,节点根据索引找到边分区上对应的边,并行实现节点参数更新。在真实和人工大规模网络数据集上的实验结果表明:LNSES在运行时间和网络结构识别准确度方面都要优于同类网络结构发现算法,可以对大规模网络中的结构进行挖掘分析。     

Parallel implementation of back-propagation algorithm in networks of workstations

This work presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained using back-propagation (BP) algorithm on network of workstations (NOWs). Hybrid partitioning (HP) scheme is used to partition the network and each partition is mapped on to processors in NOWs. We derive the processing time and memory space required to implement the parallel BP algorithm in NOWs. The performance parameters like speed-up and space reduction factor are evaluated for the HP scheme and it is compared with earlier work involving vertical partitioning (VP) scheme for mapping the MLP on NOWs. The performance of the HP scheme is evaluated by solving optical character recognition (OCR) problem in a network of ALPHA machines. The analytical and experimental performance shows that the proposed parallel algorithm has better speed-up, less communication time, and better space reduction factor than the earlier algorithm. This work also presents a simple and efficient static mapping scheme on heterogeneous system. Using divisible load scheduling theory, a closed-form expression for number of neurons assigned to each processor in the NOW is obtained. Analytical and experimental results for static mapping problem on NOWs are also presented.     

并行区域分割包分类算法

网络包分类技术是下一代路由器、防火墙、QoS保证机制实现、网络信息检测等设备的关键技术,在区域分割思想基础上，并在FPGA内实现的并行区域分割包分类算法是一种基于共享存储器和并行处理单元的高速网络包分类算法；它主要包括区域分割思想的存储器映射方法和两级、多通道并行处理技术两大部分．     

Multiprogramming and memory contention

We study memory contention during multiprogramming when programs are free to compete for page frames. A random walk between the possible partitions of memory over the set of active programs is used to model memory contention and calculate throughput. Our model of contention takes into account program characteristics by using miss ratio curves, and also considers memory size and page fetch latency. With the aid of numerous trace-driven simulations, we are able to verify our model, finding good agreement both in the observed distribution of memory among competing programs and in CPU utilization. We find that for high ratios of secondary to primary memory access time and under conditions of high memory contention, small programs with compact working sets are able to run with far less than expected interference from larger, more diffuse programs. In the case of multiprogramming the same program several times, we find that observed partition distributions are not necessarily even and that higher than expected levels of CPU use are observed. Lower ratios of access time are found to yield different results; programs compete on a more even basis and partition memory relatively more evenly than with higher ratios.     

基于扩散激活模型的无线自组网分簇方法

传统的分簇方法很少同时考虑安全因素及其对网络性能的影响。针对此问题,提出了一种基于信任关系的分簇方法,该分簇方法结合人类记忆的扩散激发模型思想,能够根据有限的局部信息自动地对整个网络进行分割。试验结果表明,该文所提出的分簇方法在精确度方面与集中式的分簇方法非常接近。因此,在提高Ad Hoc网络性能的同时,还可提高其安全性。     

基于辅助记忆循环神经网络的视角级情感分析

现有视角级情感分析方法大多数利用视角词信息从句子中提取特征,不能同时利用视角和视角词信息,导致模型性能较低,为此文中提出基于辅助记忆循环神经网络的视角级情感分析方法.首先通过深度双向长短期记忆网络和单词的位置信息构建位置权重记忆,利用注意力机制结合视角词建立视角记忆.再联合位置权重记忆和视角记忆输入多层门循环单元,得到视角情感特征.最后由归一化函数识别情感极性.实验表明,相对基准实验,文中方法在3个公开数据集上的效果更好,该方法是有效的.     

集群资源模糊聚类划分模型

提出一种集群资源模糊聚类划分模型。对计算机集群中计算节点的CPU、内存、网络、I/O和网卡资源参数进行量化和规范化,运用模糊聚类技术,实现计算节点的聚类划分。引入任务资源需求向量和最低误差容忍向量,将计算机集群划分为若干个性能均衡的逻辑子群。测试结果表明,该模型能有效划分计算机集群,适用于云计算领域的资源调度。