多媒体通信中智化媒体内同步机制

本文提出了一种智能化视频流量的预测和同步机制，它由ＢＰ神经网络流量预测器，输出缓冲区和基于模糊神经网络的输出速率器所组成。     

精馏过程的神经网络控制系统的设计

本文以一个三元精馏塔为研究对象，详细介绍了精馏过程的神经网络控制系统ＣＮＮＣＳ的设计过程，本ＮＮＣＳ主要包括ＮＮ邓估器和ＮＮ控制器，前者被训练后用来精确表达精馏塔的输入到输出的非线性映射，后者被训练后用来对该ＮＮ预估器实现解耦控制，所采用的神经网络模型为ＢＰ网络，是在一个以ＰＣ机为宿主机带９片ＴＲＡＮＳＰＵＴＥＲ的并行处理系统上实现的。     

结构自适应自组织神经网络的研究

针对Ｋｏｈｏｎｅｎ的自组织特征映射（ＳＯＦＭ）神经网络的不足,本文把进化计算的思想用于神经网络的结构寻优之中,提出了一种结构自适应的自组织神经网络（ＳＡＳＯＮＮ）模型,ＳＡＳＯＮＮ基于把每个神经元看成是一个进化群体中的一个个体的观点,构造了神经元生长（ｇｒｏｗｉｎｇ）和删除（ｐｒｕｎｉｎｇ）的准则和方法,使得ＳＯＦＭ中的神经元欠利用,神经网络映射欠准确,以及映射的边缘效应等问题得到很大程度的改善。     

N—ISDN PRA及其在S—12J的实现

Ｎ ＩＳＤＮ给用户提供两种速率接口 :基本速率接口（ＢＲＡ）和基群码率接入（ＰＲＡ）。ＢＲＡ由２个Ｂ通道（６４ｋｂ／ｓ）和１个Ｄ通道（１６ｋｂ／ｓ）组成 ;ＰＲＡ由３０个Ｂ通道（６４ｋｂ／ｓ）和１个Ｄ通道（６４ｋｂ／ｓ）组成。本文将介绍ＰＲＡ及其在Ｓ-１２Ｊ上的实现。１ＰＲＡ简介通常ＰＲＡ通过ＮＴ２ ＩＳＤＮＰＡＢＸ（ＩＳＰＢＸ）给用户提供服务 ,ＩＳＰＢＸ也是一个ＰＡＢＸ ,大部分ＩＳＰＢＸ的数据存放在ＰＡＲＭ（ＳＡＣＥＰＡＢＸ）中 ,能够通过２Ｍｂ／ｓ的ＰＲＡ接口实现点到点的连接。２Ｍｂ／ｓＰＣＭ由３０…     

HFC网管对象MIB的研究

概述了在ＨＦＣ网络中电缆设备（电缆调制解调器终端服务器、电缆调制解调器）基于ＤＯＣＳＩＳ／ＳＮＭＰ标准和规范的管理信息库（ＭＩＢ）的定义和扩充,限于篇幅,本文只涉及了其中的ＲＦ接口ＭＩＢ和电缆设备ＭＩＢ两部分,研究和掌握ＨＦＣ网络设备ＭＩＢ对实现ＨＦＣ网络业务和网络管理系统开发的意义是十分重要的。     

HFC网络QoS协议机制及MIB结构

本文概述了在ＨＦＣ网络中电缆设备（ＣＭＴＳ,Ｃ曲于ＤＯＳＣＳＩＳ／ＳＮＭＰ的服务质量（ＱｏＳ）标准规范和相应管理信息库（ＭＩＢ）的定义和扩充。ＨＦＣ网络服务质量的提出,将极大地促进ＨＦＣ业务开展。研究和掌握ＨＦＣ网络服务质量规范和相应的ＭＩＢ对实现ＨＦＣ网络业务和网络系统开发的意义是十分重要的。     

高速RS-232接口

ＰＣ的ＲＳ—２３２接口一般工作 速度为１１０ｂｐｓ（每秒位）～１９２００ ｂｐｓ。ＭＡＸＩＭ公司的几种ＲＳ—２３２收发器,可支持高达１００００００ ｂｐｓ。另外,这些收发器的输出发送器和输入接收器是ＥＳＤ保护的（＋／－１５ＫＶ）。图１是用ＤＳＰ５６Ｆ８０３（Ｍｏｔｏｒｏｌａ公司的ＤＳＰ）和ＭＡＸ３２２７（ＭＡＸＩＭ公司的高速ＲＳ—２３２）实现ＤＳＰ １Ｍ ｂｐｓＲＳ—２３２接口的设计实例。 ＤＳＰ ５６Ｆ８０３的串行控制接口（ＳＣＩ）提供发送数据输出（ＴＸＤＯ）和接收数据输入（ＲＸＤＯ）信号。其余挂钩…     

钡蒸气中放大自发辐射与参量四波混频的竞争

研究了在双光子共振条件下，Ｂａ蒸气中放大自发辐射（ＡＳＥ）与参量四波混频（ＰＦＷＭ）的竞争过程，其机制可归结为ＡＳＥ与ＰＦＷＭ之间的干涉相消。     

双向联想记忆神经网络的开关电流技术实现

本文研究了双向联想记忆（ＢＡＭ）神经网络的开关电流技术实现，提出了实现负权值及存储联想矢量的两个开关电流单元电路，基于此，给出了双向联想记忆网络的开关电流电路，文中对三神经元双向联想记忆ＳＩ网络进行了ＰＳＰＩＣＥ仿真，结果表明所提出的ＳＩ联想记忆网络是正确的。     

一种混合神经网络在颗粒图像边缘检测中的应用

本文提出了一种应用混合神经网络进行颗粒图像检测的方法。混合神经网络由用于对边缘候选图像的二值输入模式进行聚类特征提取的自组织竞争子网络（ＡＳＣＳＮＮ）和用于获取颗粒图像边缘矢量信息的ＢＰ子网络（ＢＰＳＮＮ）组成,边缘候选图像是通过采用基于灰度极小值算法提取的边缘候选象素获得。神经网络以边缘候选图像中的边缘候选象素及其邻域象素的二值模式作为训练样本。对经过噪声污染的图像进行实验表明,该方法获得的边缘图像封闭性好、边缘描述真实,抗干扰能力较强,适用于颗粒图像的边缘检测。     

Intelligent Video Traffic Smooth Mechanism for Multimedia Communication

1 Introduction Broad-band integrated service digital networks (B-ISDN) based on the asynchronous transfer mode (ATM) are designed to support a wide variety of multimedia with diverse statistical characteristics and quality of services (QoS). Among the var…     

Intelligent Video Traffic Smooth Mechanism for Multimedia Communication

A principal challenge in supporting real-time video services over ATM is the need to provide synchronous play-out in the face of stochastic end-to-end network delays. In this paper, an intelligent traffic smooth mechanism ( ITSM ) is proposed to meet the continuity requirement which is composed of a back-propagation neural network ( BPNN ) traffic predictor, a play-out buffer, and a fuzzy neural network ( FNN ) based play-out rate determinator. The BPNN traffic predictor online predicts the mean packet rate of the traffic in the future interval ( FI ) and the FNN is designed to adaptively determinate the play-out time according to the number of packets in the buffer and the traffic character predicted. Simulation results show that compared to the window mechanism, ITSM achieves high continuity with accepted delay. Furthermore, ITSM can be adaptively modified to meet the QoS of different kinds of services by FNN parameter training.     

Intelligent video smoother for multimedia communications

Multimedia communications often require intramedia synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. In this paper, we propose a neural network (NN) based intravideo synchronization mechanism, called the intelligent video smoother (IVS), operating at the application layer of the receiving end system. The IVS is composed of an NN traffic predictor, an NN window determinator, and a window-based playout smoothing algorithm. The NN traffic predictor employs an on-line-trained back-propagation neural network (BPNN) to periodically predict the characteristics of traffic modeled by a generic interrupted Bernoulli process (IBP) over a future fixed time period. With the predicted traffic characteristics, the NN window determinator determines the corresponding optimal window by means of an off-line-trained BPNN in an effort to achieve a maximum of the playout quality (Q) value. The window-based playout smoothing algorithm then dynamically adopts various playout rates according to the window and the number of packets in the buffer. Finally, we show that via simulation results and live video scenes, compared to two other playout approaches, IVS achieves high-throughput and low-discontinuity playout under a mixture of IBP arrivals     

Intelligent voice smoother for silence-suppressed voice overInternet

When transporting voice data with silence suppression over the Internet, the problem of jitter introduced from the network often renders the speech unintelligible. It is thus indispensable to offer intramedia synchronization to remove jitter while retaining minimal playout delay (PD). We propose a neural network (NN)-based intravoice synchronization mechanism, called the intelligent voice smoother (IVoS). The IVoS is composed of three components: (1) the smoother buffer; (2) the NN traffic predictor; and (3) the constant bit rate (CBR) enforcer. Newly arriving frames, assumed to follow a generic Markov modulated Bernoulli process (MMBP), are queued in the smoother buffer. The NN traffic predictor employs an online-trained back propagation NN (BPNN) to predict three traffic characteristics of every newly encountered talkspurt period. Based on the predicted characteristics, the CBR enforcer derives an adaptive buffering delay (ABD) by means of a near-optimal simple closed-form formula. It then imposes the delay on the playout of the first frame in the talkspurt period. The CBR enforcer in turn regulates CBR-based departures for the remaining frames of the talkspurt, aiming at assuring minimal mean and variance of distortion of talkspurts (DOT) and mean PD. Simulation results reveal that, compared to three other playout approaches, the IVoS achieves superior playout, yielding negligible DOT and PD, irrespective of traffic variation     

Intelligent Flow Control Technique of ABR Service in ATM Networks Based on Fuzzy Neural Networks

ＩｎｔｅｌｉｇｅｎｔＦｌｏｗＣｏｎｔｒｏｌＴｅｃｈｎｉｑｕｅｏｆＡＢＲＳｅｒｖｉｃｅｉｎＡＴＭＮｅｔｗｏｒｋｓＢａｓｅｄｏｎＦｕｚｚｙＮｅｕｒａｌＮｅｔｗｏｒｋｓ①ＺｈａｎｇＬｉａｎｇｊｉｅＬｉＹａｎｄａＬｉＱｉｎｇｈｕａＷａｎｇＰｕ（ＤｅｐｔｏｆＡ...     

基于模糊神经网络的网络业务量预测研究

利用神经网络(NN)的自学习能力以及模糊逻辑的动态性和及时性等特点,将模糊逻辑和 NN 有机地结合起来,构造出了五层模糊神经网络(FNN),并用训练 NN 的相应学习算法-BP 算法来训练网络。本文将 FNN 用于网络自相似业务预测研究中,并与单纯的 NN 算法相比较。仿真结果表明,FNN 能很好地预测复杂网络业务,与传统的 NN 算法相比,不仅收敛速度快,且得到更好的预测效果。本文为复杂网络业务流量预测研究提供了一种有效途径。     

基于神经网络的网络流量预测算法研究

网络流量预测有助于网络服务质量的提升和网络资源的合理分配,对优化网络管理与运营、保障用户体验质量至关重要。因特网业务的急剧增加和基础网络的快速发展导致网络流量变得更加复杂多样,传统网络流量预测模型难以保证较高的预测精度,而神经网络作为人工智能的重要分支,在预测复杂网络流量时具有显著优势。简述反向传播神经网络、径向基神经网络和长短期记忆神经网络的模型原理,通过分析这些神经网络预测不同时间尺度的网络流量结果,可总结其预测性能与优缺点,为基于神经网络的故障预测和故障定位的学术研究和实际应用提供技术支撑。     

基于模糊神经网络的自适应预失真功放

在无线通信中,高数据传输率的数字无线系统要求用频谱有效的线性调制方法,但是这些调制方法对功放的非线性又很敏感,会产生频谱扩展、邻近信道干扰和误码率(BER)特性的恶化。本文提出用模糊神经网络(FNN)的算法来实现功放的自适应预失真,以补偿功放的非线性,并仿真了模糊神经网络对功放非线性的补偿以及对误码率特性的改进。结果表明,此方法实现的预失真器具有良好的自适应性和鲁棒性,不需要从一大堆原始数据中进行费时的训练,而可以充分地利用己有的知识和经验;而且,在学习的过程中,采用变结构的神经网络,先粗后细、分组学习,更大大缩短了学习的时间。     

CDMA蜂窝系统中基于模糊神经网络的反向链路功率控制

CDMA是一个干扰受限系统,反向链路功率控制对于克服“远近效应”和增加系统容量是非常重要的.本文提出了一种基于模糊神经网络(FNN)的自适应闭环功率控制算法,该算法动态地调整功率控制增量,使基站接收到的每个用户的发射功率相等.仿真结果表明,由于模糊神经网络能够较好地识别反向链路的时变特性,FNN功率控制算法比传统的固定步长功率控制方法取得了更好的控制性能和更大的系统容量.而且,FNN能够通过神经网络训练自动地调整隶属度函数和模糊规则,从而适合于实现在线系统识别和自适应控制.     

Pseudo Random Number Generator Based on Back Propagation Neural Network

Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator（RNG） is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network（BPNN） is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that theRNGcan satisfy the security of communication.