topology network meaning in Chinese
网络拓朴
微电中路拓朴学
Examples
- Modeling and simulation for dynamic topology network of sis
空间信息系统动态拓扑网络建模与仿真分析 - In designing or selecting a topology for a parallel processing system , one fundamental consideration is system - level fault tolerance . in order to improve the fault tolerance , the paper analyses from the two following sides : one is by adding the less links related to the original networks , modifying the topology of the original one , we get higher fault tolerance of the new network ; the other is under the same topology network , ignoring the likelihood of one processor and ail its neighbors failing at the same time , or considering the distribution of the faulty nodes , that is studying the fault tolerance under the conditional connectivity or cluster - fault - lolerance
本文以提高网络的容错度为目的，从两个方面分析互连网络的容错性质：一是在原网络基础上增加少量连接，使新型网络具有更高的连通度(容错度为连通度减1 ) ；二是在给定互连网络拓扑结构下，考虑故障处理器发生的概率和故障处理器的分布状况，在其中的某一具体条件下，即在条件连通度和簇容错下分析互连网络的容错性能，从而得到更高的网络容错度。 - With the development of computer networks and computing science , paralleling computer and interconnection networks , covering mathematics 、 computing science 、 information science and so on , are becoming one of the hotspots of computer science research . all kinds of interconnection networks with different topologies , such as ring , mesh , hypercube , star topology network etc . , have been received rapidly development
随着计算机网络技术与计算科学的发展,并行计算机及其互连网络作为一个跨数学、计算科学与信息科学等多门学科的领域,逐渐成为计算机科学研究的热点之一,各种拓扑结构的互连网络,如环、 mesh 、超立方体、星型网络等得到迅速发展。 - This algorithm recovers the absence of the empiric in the case of the fixed - topology network and generates an optimal topology automatically . we end this chapter with some problems in the future . in chapter 2 , we present an evolution strategy to infer fuzzy finite - state automaton , the fitness function of a generated automaton with respect to the set of examples of a fuzzy language , the representation of the transition and the output of the automaton and the simple mutation operators that work on these representations are given
目前，国内外对神经网络与自动机的结合的研究己取得了一系列成果；在第一章，我们首先将对这些结果以及这个领域的研究思想与方法做一个概要的介绍；然后提出一种推导模糊有限状态自动机的构造性算法，解决了仿真实验中所给出的具体网络的隐藏层神经元个数的确定问题；在实验中，我们首先将样本输入带1个隐藏层神经元的反馈网络训练， 150个纪元以后增加神经元，此时的新网络在124纪元时收敛；而blanco [ 3 ]的固定性网络学习好相同的样本需要432个纪元。