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High-performance supra-high-speed packet-switched networking using neural arbiters.

机译:使用神经仲裁器的高性能超高速分组交换网络。

摘要

The past few years have visualized tremendous research interests along with associated breakthroughs in Optical Fiber Communications (OFCs) and Artificial Neural Networks (ANNs). The first one has resulted in the elimination of the transmission-speed bottleneck of the pre-mid-1980 era, and hence cost-effective deployment of optical fibers in both short- and long-haul telecommunication networks. And the second has led to the development of massively parallel interconnection structure with simple, i.e., of the add-compare-select type, processing elements for solving large-scale, multi-criterion decision making problems in both on-line and off-line fashions at very high speed. To utilize effectively the vast fiber bandwidth, researchers have proposed the Wavelength Division Multiplexing (WDM) technique which reduces the impact of speed mis-match between nodal processing and link transmission. Further enhancements in performance can be achieved when neurally arbitrated multi-connected regular topology--constructed using WDM--is used as the operational connection structure of any arbitrary physical topology. This dissertation deals with the development of new and efficient techniques for evaluating architecture and packet routing strategy, and enhancing performance using ANNs, of the Manhattan Street Network (MSN) which is a special kind of two-connected regular mesh topology. MSN uses uni-directional links with adjacent channels carrying packets in opposite directions. The end nodes of every row and column are directly connected using wrap-around links. A new analytical technique for evaluating the architecture of arbitrarily large MSNs in a traffic-distribution-specific manner has been developed. The validity of this technique has been verified through computer simulation. A novel simple analytical technique for evaluating deflection routing in the MSNs has also been developed. The advantage of this technique lies in the fact that although it computes the Mean Packet Transfer Time (MPTT) by simply adding the Mean Inter-Node Distance (MIND), deflection and waiting penalties, it offers a reasonable degree of accuracy as ratified by simulation. Finally, it is shown that when Grossbergu27s ANN along with a fuzzy logic based pre-processor is used for packet route arbitration, congestion-free operation of a network can be easily achieved.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses u26 Major Papers - Basement, West Bldg. / Call Number: Thesis1992 .K458. Source: Dissertation Abstracts International, Volume: 54-05, Section: B, page: 2659. Advisers: Majid Ahmadi; Malayappan Shridhar. Thesis (Ph.D.)--University of Windsor (Canada), 1992.
机译:过去几年中,随着光纤通信(OFCs)和人工神经网络(ANNs)的相关突破,人们已经看到了巨大的研究兴趣。第一个结果是消除了1980年代中期以前的传输速度瓶颈,从而在短途和长途电信网络中以经济高效的方式部署了光纤。第二个导致了大规模并行互连结构的发展,这种互连结构具有简单的即“加比较选择”类型,用于解决在线和离线大型,多准则决策问题的处理元件以很高的速度流行。为了有效利用巨大的光纤带宽,研究人员提出了波分复用(WDM)技术,该技术减少了节点处理和链路传输之间的速度失配的影响。将神经网络仲裁的多连接规则拓扑(使用WDM构造)用作任何任意物理拓扑的操作连接结构时,可以进一步提高性能。本文研究了一种新的有效技术的发展,该技术用于评估曼哈顿街网(MSN)的体系结构和数据包路由策略,并使用ANN增强性能,这是一种特殊的两连接式规则网格拓扑。 MSN使用单向链路与相邻信道以相反方向承载数据包。每行和每列的末端节点都使用环绕式链接直接连接。已经开发了一种新的分析技术,用于以特定于流量分布的方式评估任意大型MSN的体系结构。通过计算机仿真已经验证了该技术的有效性。还开发了一种新颖的简单分析技术,用于评估MSN中的偏转路径。该技术的优势在于,尽管它通过简单地添加平均节点间距离(MIND),偏转和等待惩罚来计算平均数据包传输时间(MPTT),但它提供了合理的准确度(通过仿真验证) 。最后,证明了当将Grossberg的ANN与基于模糊逻辑的预处理器一起用于数据包路由仲裁时,可以轻松实现网络的无拥塞操作。电气和计算机工程系。莱迪图书馆的纸质副本:论文主要论文-西楼地下室。 /电话号码:Thesis1992 .K458。资料来源:国际论文摘要,第54卷,第B节,第2659页。顾问:Majid Ahmadi; Malayappan Shridhar。论文(博士学位)-温莎大学(加拿大),1992。

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    Khasnabish Bhumip.;

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