光学神经网络的现状与进展

通过介绍两种典型的光学神经网络ＯＮＮ－透镜阵列互连网络和全息互 网张,讨论了不前ＯＮＮ的现状,存在问题及发展趋势。还介绍了图样间联想和洗牌网等ＯＮＮ模型中的新算法和新拓扑结构。     

人工神经网络及其在化学领域中的应用

简化了人工神经网络的定义及分类,着重介绍了ＡＮＮ中一种较经典的网络模型－ＢＰ网络模型的网络结构和学习原理,总结了ＡＮＮ在化学领域中的应用,包括谱图分析,药物分子等。同时讨论了在使用ＡＮＮ方法时应注意的几个问题,如隐层节点数目的确定等。     

一种用于机群系统的双环光互连网络性能分析

高性能计算机网络对带宽的需求,使得如何提供高带宽的互连网络以及充分利用互连网络的固有带宽度为一个研究焦点。基于Linux操作系统,以千兆光互连接口卡为网络接口,设计实现了一种可用于机群系统的双环光互连网络。介绍了光互连网接口卡的基本结构,接口卡驱动软件的设计方法,双环光互连网络的拓朴结构及其特性。分析测试了光互连网络的通信性能,指出了影响系统整体性能的关键因素。     

近线性神经元和非线性神经元

人工神经网络（ＡＮＮ）中普遍采用的神经元模型具有单调有界的Ｉ／Ｏ特性,该文称其为近线性神经元（ＡＬＮ,ａｐｐｒｏｘｉｍａｔｅ－ｌｉｎｅ ｎｅｕｒｏｎ）,ＡＬＮ既简化了网络和复杂性,也是限制ＡＮＮ能力的一个瓶径。通过比较Ｈｏｐｒｉｅｌｄ;神经网络的稳定性和Ｃｏｎｗａｙ生命游戏的复杂行为,该文提出了非线性神经元（ＮＬＮ〉ｎｏｎ－ｌｉｅｎｅｒ ｎｅｕｒｏｎ）的概念,其Ｉ／Ｏ特性是完全线性的。ＡＬＮ可以进     

一种用于并行计算系统的分级光环互连网络

介绍了大规模并行计算的一个新互连网络——分级光环互连,适用于多处理器与多计算机的可升级网络。分级光环互连由一个衡量无阻塞、容错的单跳可升级互连拓扑组成,并通过波分多址技术充分地利用了光纤的TeraHz带宽。此光网络融合了分级环的互连节点接口简单、节点度恒定、容错等各种吸引人的特征以及光通信的各种优点。提出了分级光环互连拓扑,分析了其结构特征并描述了光设计的方法,导出了一个简短可行的分级光环互连研究。     

TCP／IP协议网络中远程用户功能的实现与应用

在异种机、异种操作系统互连的网络中，广泛采用ＴＣＰ／ＩＰ通信协议，局域网络的ＵＮＮ服务器硬、软资源丰富，为多任务系统，能同时响应多个站点的服务请求，而ＤＯＳ或ＸＥＮＩＸ网络工作站，硬、软资源相对贫乏，但可借助ＴＣＰ／ＩＰ协议网络的远程用户功能得以补偿。本文结合网络管理信息系统实例对其进行阐述。     

面向HPC和DC的可重构光互连网络体系结构综述

互连网络是高性能计算系统和数据中心的核心组件之一,也是决定其系统整体性能的全局性基础设施。随着高性能计算、云计算和大数据技术的迅速发展,传统的电互连网络在性能、能耗和成本等方面无法满足高性能计算应用和数据中心业务的大规模可扩展通信需求,面临着严峻的挑战。为此,近年来相关研究者提出了多种面向高性能计算和数据中心的可重构的光互连网络结构。首先阐明了光互连网络相对于电互连网络的优势;然后介绍了几种典型的可重构光互连网络体系结构,并对其特点进行了分析比较;最后探讨了可重构光互连网络的发展趋势。     

多级光互连多计算机系统结构设计

本文从我国当前微光电子和微电子的研究与生产水平出发，提出并设计了一个可扩展性特别好的２×４×１６光互连基本模块（ＯＩＭ）．通过多个ＯＩＭ的组构可以实现一些不同结构的多级光互连计算机并行处理系统     

CONET V2.0ATM网络互连机制

ＡＴＭ是一种较新的网络技术,它使得高速的语音、图像,数据等多媒体信息传输成为可能,与各种现有的网络互连是ＡＴＭ得以普及应用的关键,本文首先提出ＡＴＭ网络互连中需要解决的问题,然后介绍了国内网络系统ＣＯＮＥＴ Ｖ２．０的ＡＴＭ网络互连机制,并给出了实现中的一些关键算法。     

自适应模糊神经网络控制器设计的线性化方法

基于Ｔ－Ｓ模糊推理系统模型构造一个简化形式的Ｆｕｚｚｙ神经网络（ＦＮＮ），应用Ｓｔｏｎｅ－Ｗｅｉｅｒｓｔｒａｓｓ逼近定理证明了这种ＦＮＮ网络对非线性连续函数的全局逼近性质，并利用Ｃｌａｒｋｅ一步加权最优预报控制性能指标及前向ＦＮＮ网络辨识器模型的线性化思想，提出一种间接Ｆｕｚｚｙ神经网络自适应控制算法，仿真结果证实了该算法的有效性。     

有序神经网络及在阳极效应预报中的应用

提出了利用有序神经网络研究铝电解槽阳极效应的预报问题。概述了铝电解槽及其阳极效应的基本情况，针对铝电解槽控制难题和传统方法的不足，选择有序神经网络用于阳极效应概率预报。描述了有序神经网络的基本结构、与传统单隐层BP神经网络的区别以及由此带来的网络映射性能的改善，并使用梯度下降原则推导了有序神经网络的学习算法。使用铝电解槽的现场数据对有序神经网络进行训练并检验，结果表明有序神经网络可以比传统神经网络更及时、准确地对铝电解槽阳极效应进行预报。     

Properties and performance of orthogonal neural network in function approximation

Backpropagation neural network has been applied successfully to solving uncertain problems in many fields. However, unsolved drawbacks still exist such as the problems of local minimum, slow convergence speed, and the determination of initial weights and the number of processing elements. In this paper, we introduce a single‐layer orthogonal neural network (ONN) that is developed based on orthogonal functions. Since the processing elements are orthogonal to one another and there is no local minimum of the error function, the orthogonal neural network is able to avoid the above problems. Among the five existing orthogonal functions, Legendre polynomials and Chebyshev polynomials of the first kind have the properties of recursion and completeness. They are the most suitable to generate the neural network. Some typical examples are given to show their performance in function approximation. The results show that ONN has excellent convergence performance. Moreover, ONN is capable of approximating the mathematic model of backpropagation neural network. Therefore, it should be able to be applied to various applications that backpropagation neural network is suitable to solve. © 2001 John Wiley & Sons, Inc.     

一种光传送网的建模及其价值评估

光纤通信技术(optical fiber communications)从光通信中脱颖而出,已成为现代通信的主要支柱之一,在现代电信网中起着举足轻重的作用。光纤通信的优点是通信容量大,传输距离远,信号干扰小,保密性好,抗电磁干扰等。本文在综合考虑传输距离,传输容量、网络拓扑等各种因素的情况下,对光传送链路进行了简单建模,制定了光传送网的规划,并探讨了其网络价值。     

An optical power budget model for the Parameterized Linear Array with a Reconfigurable Pipelined Bus System (LARPBS(p)) model

The Parameterized Linear Array with a Reconfigurable Pipelined Bus System (LARPBS(p)) model consists of a virtual machine architecture, routing and addressing, communication primitives and parameterization for algorithm cost and comparison. Unique features of this model over the earlier LARPBS model include its execution time parameterization of the model’s virtual machine abstraction as well as two new communication primitives. However, both models share similar architectures. A passive optical fiber-based implementation of the abstract machine architecture for the LARPBS(p) and LARPBS models is described. The feasibility of selected device implementation is assessed through the development of an optical power budget model that includes estimation for the signal loss, power budget, and the maximum number of supported processors. The model is applied to four case studies. Based on these case studies, a further scalability analysis is conducted. Lastly, comments are made about the achievable data rates while maintaining a low BER.     

全光通信网及其发展现状

全光网是未来宽带网的发展目标,它是针对目前通信网中所存在的电子瓶颈效应而提出的。文中首先介绍了全光网的概念及其网络结构,全光网的优点以及全光网可提供的业务,然后介绍了全光通信网的关键技术及其目前的进展状况。     

浅谈光纤通信新技术的应用与研究

在长途通信网和市话通信网中光纤通信网代替电缆通信网已成为被世界所公认的事实。此时因为通信距离影响,导致利用率的不断下降,所以为光纤通信取得进一步发展,必须寻找新的途径,采用新的技术。本文首先简要概述光纤通信技术特点,然后阐明光纤通信新技术的实践应用,最后进行对光纤通信新技术的研究探讨。     

Time-, wavelength-, and code-domain optical reflection monitoring for next-generation access-metro networks

Distributed optical reflectors are proposed to implement essential fault management operations such as fault detection, localization, and notification, in next generation all-optical access-metro networks in the optical layer. Fixed time-slots, wavelengths, or optical codes are assigned to selected key network locations along the path of a monitoring signal where corresponding mirrors are placed. The reflection received at a transmitting node enables online all-optical monitoring of the selected locations. After detailing the network architecture, we explore fundamental system design issues such as real-time fault localization algorithms, fault notification delay upperbounds, and delay line calculation algorithms for synchronous operation. Our simulation results showcase standard and long-reach passive optical networks, and demonstrate that our algorithms achieve fault notification at light speed using off-the-shelf components.     

Multimodal biometric cryptosystem for human authentication using fingerprint and ear

The multimodal biometrics is mainly used for the purpose of person certification and proof. Lot of biometrics is used for human authentication. In which ear and fingerprint are efficient one. There are three vital phases involved in the biometric detection which include the Preprocessing, Feature extraction and the classification. Initially, preprocessing is done with the help of median filter which lends a helping hand to the task of cropping the image for choosing the position. Then, from the preprocessed Finger print and ear image texture and shape features are extracted. In the long run, the extracted features are integrated. The integrated features, in turn, are proficiently classified by means of the optimal neural network (ONN). Here, the NN weights are optimally, selected with the help of firefly algorithm (FF). The biometric image is classified into fingerprint and ear if the identical person images are amassed in one group and the uneven images are stored in a different group. The performance of the proposed approach is analyzed in terms of evaluation metrics.