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（ＯｐｔｉｃａｌＮｅｕｒａｌＮｅｔｗ ｏｒｋ）——透镜阵列互连网络和全息互连网络,讨论了当前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。     

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     

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。     

基于神经网络的系统优化与参数估计集成研究（ISOPE）方法

提出了两种基于神经网络改进的系统优化与参数估计集成稳态优化算法，其中利用动态信息建立动态ＮＮ模型用于过程稳态优化。目的是为了克服ＩＳＯＰＥ算法对真实过程的摄动，减少ＩＳＯＰＥ算法设定点变动次数，充分利用过程动态信息，仿真结果验证了两种改进算法的优越性和有效性。     

过程辨识和控制中神经网络结构与方法

本文分析了人工神经网络在过程控制和辨识领域的多种结构与方法，重点阐述了在线自适应神经网络控制器（ＯＡＮＮＣ）的结构和算法，并对神经网络在过程控制中应用的前景作了展望。     

杀幼（虫）剂1－（Substitutedbenzoyl）－2－benzoyl－1－tert－butylhydrazines的QSAR研究

使用ＣＡＳＡＣ软件中的逐步回归和改进的神经网络（ＡＮＮ）方法，对Ｎ．Ｏｉｋａｗａ等研究合成的杀幼（虫）剂进行了计算．其中，４４个化合物作为训练基，１１个化合物作为未知样本，获得良好的预报结果，与Ｎ．Ｏｉｋａｗａ等使用Ｈａｎｓｃｈ方法计算所取得的结论一致．不同的是本文所使用的物化参数除Ｂ５和Ｌ１之外，都能够方便的计算．改进的神经网络（ＡＮＮ）方法提高了模型质量和预报结果的精度．     

ANN的一种存储器实现

针对数字电路（算术单元）实现ＡＮＮ时执行时间长和成本高的缺点，本文提出了一种基于存储器结构的ＡＮＮ实现，通过广播式写入可克服学习困难、低精度和复杂性等问题。因此对于小规模实时应用，基于存储器结构的ＡＮＮ是可行的。     

NNKBST：神经网络知识库系统支撑环境

为了有效地解决知识获取这一瓶颈问题，本文提出用神经网络来进行知识获取，以弥补传统归纳学习方法的不足，并以ＮＮ知识获取技术为核心，扩展为ＮＮ知识库系统支撑环境中一些全面技术的研究，包括ＮＮ知识表示和问题求解机制，ＮＮ知识库的推理，维护和求精机制等。     

神经网络问题求解机制

本文提出了神经网络知识表示的形式化描述语言和知识单元的概念，用于给出传统符号逻辑中的概念，用于给出传统符号逻辑中的概念，属性及它们之间的层次关系如何在神经网络中进行表示，提出了激活强度的度量标准，从而在理论上给出了ＮＮ中继承和识别问题的形式化处理方法。在此基础上，提出了ＮＮ正向和反向问题求解机制。这里，值得一提的是，本文通过在某一特定领域中对ＮＮ推理机制的成功探讨，有力地反驳了人们对于ＮＮ模型的一     

人脸全局特征识别研究

人脸识别是模式中的一个相当重要却又十分困难的课题。本文利用神经网络（Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋ，简称ＮＮ）及主元分析法（Ｐｒｉｎｃｉｐｌｅ Ｃｏｍｐｏｎｅｎｔ Ａｎａｌｙｓｉｓ），简称ＰＣＡ）不同的特性提出了两种人脸识别的模型：ＮＮ＋ＮＮ模型及ＰＣＡ＋ＮＮ的模型。理论分析和实验结果表示：这两种新的识别模型可以实现优化特征抽取和自适应识别。     

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.     

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。     

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。     

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.