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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。 相似文献
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精馏过程的神经网络控制系统的设计 总被引:1,自引:0,他引:1
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。 相似文献
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结构自适应自组织神经网络的研究 总被引:10,自引:0,他引:10
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。 相似文献
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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… 相似文献
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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。 相似文献
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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。 相似文献
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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… 相似文献
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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。 相似文献
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一种混合神经网络在颗粒图像边缘检测中的应用 总被引:1,自引:0,他引:1
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。 相似文献
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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… 相似文献
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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. 相似文献
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Intelligent video smoother for multimedia communications 总被引:1,自引:0,他引:1
Yuang M.C. Po L. Tien Shih T. Liang 《Selected Areas in Communications, IEEE Journal on》1997,15(2):136-146
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 相似文献
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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 相似文献
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InteligentFlowControlTechniqueofABRServiceinATMNetworksBasedonFuzzyNeuralNetworks①ZhangLiangjieLiYandaLiQinghuaWangPu(DeptofA... 相似文献
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基于模糊神经网络的自适应预失真功放 总被引:1,自引:0,他引:1
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。 相似文献
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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. 相似文献
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WANG Bang-ju WANG Yu-hua NIU Li-ping ZHANG Huan-guo 《半导体光子学与技术》2007,13(2):164-168
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. 相似文献