一种基于LVQ神经网络的聚类方法

现在学习矢量量化（Learning Vector Quantization,LVQ）网络在聚类分析中得到了广泛的应用．但是该类网络还有需要完善的地方。本文通过在LVQ网络的训练过程中引入阈值学习规则,提出了一种改进的算法。较好的解决了该类网络中遇到“死”点时不能对数据进行正确分类的问题。最后通过Matlab7．0仿真实验表明该算法与传统的LVQ网络相比具有更好的聚类效果。     

对学习矢量量化神经网络中“死”点问题的研究

竞争型神经网络已经在模式识别、分类等方面得到了广泛的应用,与传统的聚类方法相比具有巨大优势,但是在许多方面还存在不足,需要进一步完善。在Kohonen提出的学习矢量量化网络(Learning Vector Quantization Network,LVQ)的基础上,引入阈值学习规则,较好地解决了该类网络中遇到"死"点时训练误差偏大的问题,最后通过Matlab编程实现。     

矢量量化的误差竞争学习算法

提出了误差竞争学习（Ｄｉｓｔｏｒｔｉｏｎ ｃｏｐｍｐｅｔｉｔｉｖｅ ｌｅａｒｎｉｎｇ,ＤＣＬ）算法。该算法基于Ｇｅｒｓｈｏ的矢量量化误差渐近理论的等误差原则,即当码本数趋于无穷大时,各区域子误差相等,使用这个原则作为最优码书设计的一个必要条件,并结合传统最优码书设计的两个必要条件,然后根据这３个必要条件：（１）最近邻规则;（２）中心准则;（３）各区域了误差近似相等设计最优码书,而在算法的实现中引入     

图象压缩的模糊竞争矢量量化方法

在分析神经网络竞争学习算法和模糊Ｃ均值算法的基础上，提出了模糊竞争学习算法，并对模糊隶属度函数进行了探讨。理论分析和实验结果表明，模糊竞争学习算法用于图象矢量量化压缩编码是一种非常有效的方法。     

用于矢量量化的神经网络竞争学习算法

对典型的竞争学习算法进行了研究和分析，提出了一种基于神经元获胜概率的概率敏感竞争虎法。与传统竞争学习算法只有一个神经元获胜而得到学习不同，ＰＳＣＬ算法按照各种凶的获胜概率并通过对失真距离的调整使每个神经元均得到不同的学习，可以有效地克服神经元欠利用问题。     

熵约束广义学习矢量量化神经网络和软竞争学习算法

结合广义学习矢量量化神经网络的思想和信息论中的极大熵原理,提出了一种熵约束 广义学习矢量量化神经网络,利用梯度下降法导出其学习算法,该算法是软竞争格式的一种推 广.由于亏损因子和尺度函数被定义为同一个模糊隶属度函数,它可以有效地克服广义学习矢 量量化网络的模糊算法存在的问题.文中还给出熵约束广义学习矢量量化网络及其软竞争学习 算法的许多重要性质,以此为依据,讨论拉格朗日乘子的选取规则.     

等误差竞争学习算法在矢量量化中的应用

提出了一种使各区域子误差相等的矢量量化算法,算法利用小波变换后各子带间的相关性,合理构造矢量。采用最优矢量量化器设计原则,通过调整学习过程中各子区域的误差,使之趋于相等,改善总的期望误差,获得更接近全局最优的码书。实验表明,这种算法获得的码本优于其它几种算法。     

遗传选择机制在矢量量化中的应用

提出误差选择竞争学习算法,它把遗传算法中的选择机制引入到矢量量化设计中,在使用竞争学习算法减小期望误差的前提下,利用选择机制调整各个区域的子误差从而进一步改善期望误差,实验结果表明,该算法较好地调整了各区域的子误差,克服局部最优     

基于学习矢量量化网络的智能BIT故障诊断方法

将一种经过修正的基于学习矢量量化算法的竞争网络应用在多电飞机电气系统智能BIT故障诊断中,该网络在竞争层实现故障模式的自组织聚类,在输出层给出了具体的故障模式,通过与原算法进行比较,修正后的算法达到了很好的故障识别和分类效果.     

一种彩色图像矢量误差分散半调方法

误差分散是数字半调技术中的一项关键技术。通过对矢量彩色图像误差分散半调的分析,结合误差分散系统中量化器的建模,提出了一种增强边缘信息并减少虫状纹理的矢量彩色图像误差分散半调方法。     

A winning strategy for managing simulation projects

In this paper a winning strategy for conducting successful simulation studies is proposed. The proposed approach emphasizes risk reduction through incremental model development and encourages user commitment through team work. A case study, based on a medium-sized manufacturing facility in Missouri, is used to illustrate its application. This successful strategy can be easily adopted on other similar simulation studies.     

一种动态的入侵检测系统负载均衡算法

目前的入侵检测不仅需要模式匹配,而且需要协议异常检测,提出了一种新动态的负载均衡算法,采用两层结构,对网络流量按照服务类型进行初步划分之后分别对每部分流量进行二次分配,并对每种类型的流量进行相应的协议异常检测。该算法能在不牺牲系统性能的前提下有效提高网络入侵检测系统的检测效率,降低误检率,并可有效地适应网络流量的变化,降低漏检率。     

Disk load balancing for video-on-demand systems

For a video-on-demand computer system, we propose a scheme which balances the load on the disks, thereby helping to solve a performance problem crucial to achieving maximal video throughput. Our load-balancing scheme consists of two components. The static component determines good assignments of videos to groups of striped disks. The dynamic component uses these assignments, and features a “DASD dancing” algorithm which performs real-time disk scheduling in an effective manner. Our scheme works synergistically with disk striping. We examine the performance of the proposed algorithm via simulation experiments.     

Load balancing schemes for extrapolation methods

Solving initial value problems (IVPs) for ordinary differential equations (ODEs) has long been believed to be an inherently sequential procedure. But IVP solvers using the extrapolation method provide high quality solutions and offer a great potential for parallelism. In this paper, we present algorithms for extrapolation methods on distributed memory multiprocessors that combine different levels of parallelism. These algorithms differ mainly in the partitioning of the processors into groups which are responsible for the execution of the independent tasks of the extrapolation method. We present the algorithms in a compute–communicate scheme using appropriate primitives for the communication. A detailed analysis shows that a sophisticated load balancing scheme is required to achieve good speedup. We describe an optimal method based on Lagrange multipliers, investigate several simple heuristic schemes, and compare the heuristic schemes with an estimation for the optimal solution. An implementation of these schemes on an Intel iPSC\860 confirms the predicted runtimes. © 1997 by John Wiley & Sons, Ltd.     

Distributed algorithms for QoS load balancing

We consider a dynamic load balancing scenario in which users allocate resources in a non-cooperative and selfish fashion. The perceived performance of a resource for a user decreases with the number of users that allocate the resource. In our dynamic, concurrent model, users may reallocate resources in a round-based fashion. As opposed to various settings analyzed in the literature, we assume that users have quality of service demands. A user has zero utility when falling short of a certain minimum performance threshold and having positive utility otherwise. Whereas various load-balancing protocols have been proposed for the setting without quality of service requirements, we consider protocols that satisfy an additional locality constraint: The behavior of a user depends merely on the state of the resource it currently allocates. This property is particularly useful in scenarios where the state of other resources is not readily accessible. For instance, if resources represent channels in a mobile network, then accessing channel information may require time-intensive measurements. We consider several variants of the model, where the quality of service demands may depend on the user, the resource, or both. For all cases we present protocols for which the dynamics converge to a state in which all users are satisfied. More importantly, the time to reach such a state scales nicely. It is only logarithmic in the number of users, which makes our protocols applicable in large-scale systems.     

A case for on-machine load balancing

This paper diverges from the traditional load balancing, and introduces a new principle called the on-machine load balance rule. The on-machine load balance rule leads to resource allocations that are better in tolerating uncertainties in the processing times of the tasks allocated to the resources when compared to other resource allocations that are derived using the conventional “across-the-machines” load balancing rule. The on-machine load balance rule calls for the resource allocation algorithms to allocate similarly sized tasks on a machine (in addition to optimizing some primary performance measures such as estimated makespan and average response time). The on-machine load balance rule is very different from the usual across-the-machines load balance rule that strives to balance load across resources so that all resources have similar finishing times.We give a mathematical justification for the on-machine load balance rule requiring only liberal assumptions about task processing times. Then we validate with extensive simulations that the resource allocations derived using on-machine load balance rule are indeed more tolerant of uncertain task processing times.     

Partitioning of unstructured meshes for load balancing

Many large-scale engineering and scientific calculations involve repeated updating of variables on an unstructured mesh. To do these types of computations on distributed memory parallel computers, it is necessary to partition the mesh among the processors so that the load balance is maximized and interprocessor communication time is minimized. This can be approximated by the problem of partitioning a graph so as to obtain a minimum cut, a well-studied combinatorial optimization problem. Graph partitioning is NP complete, so for real world applications one resorts to heuristics, i.e. algorithms that give good but not necessarily optimum solutions. These algorithms include recursive spectral bisection, local search methods such as Kernighan-Lin, and more general purpose methods such as simulated annealing. We show that a general procedure enables us to combine simulating annealing with Kernighan-Lin. The resulting algorithm is both very fast and extremely effective.     

Semi-distributed load balancing for massively parallelmulticomputer systems

A semidistributed approach is given for load balancing in large parallel and distributed systems which is different from the conventional centralized and fully distributed approaches. The proposed strategy uses a two-level hierarchical control by partitioning the interconnection structure of a distributed or multiprocessor system into independent symmetric regions (spheres) centered at some control points. The central points, called schedulers, optimally schedule tasks within their spheres and maintain state information with low overhead. The authors consider interconnection structures belonging to a number of families of distance transitive graphs for evaluation, and, using their algebraic characteristics, show that identification of spheres and their scheduling points is in general an NP-complete problem. An efficient solution for this problem is presented by making exclusive use of a combinatorial structure known as the Hadamard matrix. The performance of the proposed strategy has been evaluated and compared with an efficient fully distributed strategy through an extensive simulation study. The proposed strategy yielded much better results     

Game-theoretic static load balancing for distributed systems

In this paper, we present a game theoretic approach to solve the static load balancing problem for single-class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network. The objective of our approach is to provide fairness to all the jobs (in a single-class system) and the users of the jobs (in a multi-user system). To provide fairness to all the jobs in the system, we use a cooperative game to model the load balancing problem. Our solution is based on the Nash Bargaining Solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. To provide fairness to all the users in the system, the load balancing problem is formulated as a non-cooperative game among the users who try to minimize the expected response time of their own jobs. We use the concept of Nash equilibrium as the solution of our non-cooperative game and derive a distributed algorithm for computing it. Our schemes are compared with other existing schemes using simulations with various system loads and configurations. We show that our schemes perform near the system optimal schemes and are superior to the other schemes in terms of fairness.     

Offline file assignments for online load balancing

We study a novel load balancing problem that arises in web search engines. The problem is a combination of an offline assignment problem, where files need to be (copied and) assigned to machines, and an online load balancing problem, where requests ask for specific files and need to be assigned to a corresponding machine, whose load is increased by this.We present simple deterministic algorithms for this problem and exhibit an interesting trade-off between the available space to make file copies and the obtainable makespan. We also give non-trivial lower bounds for a large class of deterministic algorithms and present a randomized algorithm that beats these bounds with high probability.