噪声干扰下NGB接入网上行信道性能与智能诊断研究
|
摘要
广播电视网是目前国内最普及的信息传播载体和重要的舆论宣传阵地，在未来国家信息化基础建设中具有举足轻重的地位和作用。但随着信息技术的快速发展，广大群众获取信息的渠道越来越多，获得的信息也越来越丰富，特别是2010年初，国家制定了关于加快推进电信网、广播电视网和互联网三网融合的战略计划，并明确了三网融合的时间表。为了适应新的形势，必须要引入先进的技术和理念对现有的电视系统进行改造，以满足广大群众对现代数字媒体和信息服务的需求，由此下一代广播电视网络应时而生。下一代广播电视网络是数字电视广播技术和数字信息技术相结合的产物，它由电信网、计算机网和有线电视网三网融合而成，用户可以按照自己的需求获取多种信息，从根本上改变广播电视“你播我收”的主从关系，形成全新的媒体服务模式。与此同时，为了满足下一代广播电视网络建设和发展的需要，打破高端专用仪器被国外企业垄断的局面，国内相关的测试与检测理论和方法成为研究的热点，多种专用仪器开发已被列入议事日程。
本文基于开发下一代有线广播电视网络监控系统的需求，针对下一代有线电视网络的噪声特性和故障检测进行了深入研究。鉴于下行信道性能的研究已经非常成熟，本文将重点放在：下一代有线广播电视网络上行信道噪声模型详细推导与建立、基于该噪声模型的OFDM基带系统仿真平台构建和采用神经网络技术的上行信道故障分析检测系统研究与开发。主要创新性工作如下：
接入网技术和多载波通信技术是下一代广播电视网的核心技术。本文在分析、研究接入网技术相关协议标准的基础上，重点研究了包括高斯噪声、窄带连续波噪声、脉冲噪声的特征函数模型和误码率表达式，以及基于OFDM技术的下一代有线广播电视网络性能。基于Matlab/Simulink软件，首先构建了包括信号源、调制解调、OFDM以及误码率计算等在内的多种模块；其次结合典型噪声特性模型和建模技术构成基带OFDM系统仿真平台；最后通过该平台进行了OFDM系统仿真和通信系统的性能分析，得到了仿真结果，从而为深入研究下一代广播电视网络性能，开发具有自主产权的专用宽带网络监控系统奠定了坚实的基础。
在上述研究成果的基础上，将BP神经网络技术运用到下一代有线广播电视网上行信道故障诊断识别，通过提取上行信道的频谱特征作为神经网络的输入、采用实际上行信道频谱数据训练神经网络，获得噪声或干扰类型。经过大量仿真，得到最适合下一代有线广播电视网上行信道故障诊断的学习算法和激活函数，并针对该算法和激活函数推导出了权值修正量。
按照本文推导的噪声模型等表达式、搭建的多载波有线广播电视通信系统仿真平台和基于神经网络技术开发的下一代有线广播电视网上行信道故障诊断识别系统，设计了NGB有线电视网上行信道回传噪声监测系统方案并已嵌入到德力电子有限公司的相关产品，为最终产品定型生产提供了重要的核心技术支撑。经实际使用，基于BP神经网络算法的故障诊断系统的故障诊断准确率达到85%以上，证明本文获得的噪声模型、仿真平台和故障诊断算法等成果的正确性，为开发具有自主知识产权的高端下一代广播电视网络专用仪器做出了贡献。
Radio&TV is the most popular carrier of transmitting information and also is themost important platform of propagating public opinions. It will play an important rolein the national information technology infrastructure in the future. With the rapiddevelopment of IT, more and more access to information channels has beenestablished and more and more rich information obtained by the masses. Especially inearly2010, the nation has made a plan about accelerating the telecommunicationnetwork, broadcast network and internet network integration strategic plan and clearlymade a network integration schedule. In order to adapt to the new situation andmeet the needs of the masses on digital media and information services, we mustintroduce advanced techniques and concepts to transform the existing TV system. Thenext generation broadcast network comes into being. The next generation broadcastnetwork is a product of digital TV broadcasting technology and digital informationtechnology integration, and it consists of telecommunication network, computernetwork and cable television network. Users can access to a variety of information inaccordance with their own needs, and it fundamentally change the master-slaverelationship "you broadcast and I receive" between radio and television and form anew media services. At the same time, in order to meet the needs of construction anddevelopment of the next generation broadcast network and break the high-endequipment monopolized by foreign business, the theory and method related to test anddetection has become a hot research in the domestic, and a variety of specialinstrument development have already been included in the schedule.
In this paper, based on the needs for development of the next generation cablebroadcast network monitor system, we deeply studied noise characteristics and faultdetection. In view of downlink channel performance research has been mature, thispaper will focus on: detailed derivation and build of the next generation cablebroadcast network uplink channel noise model, the research and development ofOFDM base-band system simulation platform and uplink channel failure analysisdetection system using neural network technology. The main innovation works are asfollows:
Access network technology and multi-carrier communication is the coretechnology for the next generation broadcast networks. Based on the analysis andstudy of the related access network technique protocol standard, this dissertation focuses on the characteristic function model including Gaussian noise, narrowbandcontinuous wave noise, pulse noise and BER expression, and the performance of nextgeneration cable television network based on the OFDM technique. UsingMatlab/Simulink software, we firstly constructed some modules such as the signalsource, modulation and demodulation, OFDM and BER calculation. Then base-bandOFDM systems simulation platform is formed using typical noise model andmodeling technology. Finally the simulation results are obtained by simulating theOFDM systems and analyzing the performance of communication system at theplatform. It lays solid foundation for further researching the next generation broadcastnetwork performance and developing special broadband network monitor system withindependent property.
Based on the above research results, BP neural network technique can be used inthe area of the next generation cable broadcast and TV network for upstream channelfault diagnosis. Spectrum characteristics of upstream channel are used as neuralnetwork inputs. Upstream channel spectrum data obtained by actual measurement isused to train the neural network so that noise and interference type can be obtained byanalyzing the neural network output. After a lot of simulations, the most suitablelearning algorithm and activation function of the BP neural network can be obtainedto be used for upstream channel fault diagnosis of the next generation cable broadcastand TV network. The corrections of the weights were also derived in this thesis basedon the most suitable algorithm and activation function.
Based on noise model expression derived by this thesis, upstream channel faultdiagnosis identification system for the next generation cable broadcast and TVnetwork can be built as well as cable broadcast and TV communication multi-carriersystem simulation platform. We designed a scheme of upstream channel noisemonitoring system for the next generation cable broadcast and TV network, which hasbeen embedded into the related products of the Deli Electronics Limited Company. Itplays a core technical role for the final product. Proved by actual application, theaccuracy of the fault diagnosis system based on BP neural network is more than85%.It verifies the correctness of the noise model, simulation platform and fault diagnosisalgorithm. We hope to contribute to the development of the next generation high-endbroadcast and TV network instrument for a special purpose with independentintellectual property rights.
本文基于开发下一代有线广播电视网络监控系统的需求，针对下一代有线电视网络的噪声特性和故障检测进行了深入研究。鉴于下行信道性能的研究已经非常成熟，本文将重点放在：下一代有线广播电视网络上行信道噪声模型详细推导与建立、基于该噪声模型的OFDM基带系统仿真平台构建和采用神经网络技术的上行信道故障分析检测系统研究与开发。主要创新性工作如下：
接入网技术和多载波通信技术是下一代广播电视网的核心技术。本文在分析、研究接入网技术相关协议标准的基础上，重点研究了包括高斯噪声、窄带连续波噪声、脉冲噪声的特征函数模型和误码率表达式，以及基于OFDM技术的下一代有线广播电视网络性能。基于Matlab/Simulink软件，首先构建了包括信号源、调制解调、OFDM以及误码率计算等在内的多种模块；其次结合典型噪声特性模型和建模技术构成基带OFDM系统仿真平台；最后通过该平台进行了OFDM系统仿真和通信系统的性能分析，得到了仿真结果，从而为深入研究下一代广播电视网络性能，开发具有自主产权的专用宽带网络监控系统奠定了坚实的基础。
在上述研究成果的基础上，将BP神经网络技术运用到下一代有线广播电视网上行信道故障诊断识别，通过提取上行信道的频谱特征作为神经网络的输入、采用实际上行信道频谱数据训练神经网络，获得噪声或干扰类型。经过大量仿真，得到最适合下一代有线广播电视网上行信道故障诊断的学习算法和激活函数，并针对该算法和激活函数推导出了权值修正量。
按照本文推导的噪声模型等表达式、搭建的多载波有线广播电视通信系统仿真平台和基于神经网络技术开发的下一代有线广播电视网上行信道故障诊断识别系统，设计了NGB有线电视网上行信道回传噪声监测系统方案并已嵌入到德力电子有限公司的相关产品，为最终产品定型生产提供了重要的核心技术支撑。经实际使用，基于BP神经网络算法的故障诊断系统的故障诊断准确率达到85%以上，证明本文获得的噪声模型、仿真平台和故障诊断算法等成果的正确性，为开发具有自主知识产权的高端下一代广播电视网络专用仪器做出了贡献。
Radio&TV is the most popular carrier of transmitting information and also is themost important platform of propagating public opinions. It will play an important rolein the national information technology infrastructure in the future. With the rapiddevelopment of IT, more and more access to information channels has beenestablished and more and more rich information obtained by the masses. Especially inearly2010, the nation has made a plan about accelerating the telecommunicationnetwork, broadcast network and internet network integration strategic plan and clearlymade a network integration schedule. In order to adapt to the new situation andmeet the needs of the masses on digital media and information services, we mustintroduce advanced techniques and concepts to transform the existing TV system. Thenext generation broadcast network comes into being. The next generation broadcastnetwork is a product of digital TV broadcasting technology and digital informationtechnology integration, and it consists of telecommunication network, computernetwork and cable television network. Users can access to a variety of information inaccordance with their own needs, and it fundamentally change the master-slaverelationship "you broadcast and I receive" between radio and television and form anew media services. At the same time, in order to meet the needs of construction anddevelopment of the next generation broadcast network and break the high-endequipment monopolized by foreign business, the theory and method related to test anddetection has become a hot research in the domestic, and a variety of specialinstrument development have already been included in the schedule.
In this paper, based on the needs for development of the next generation cablebroadcast network monitor system, we deeply studied noise characteristics and faultdetection. In view of downlink channel performance research has been mature, thispaper will focus on: detailed derivation and build of the next generation cablebroadcast network uplink channel noise model, the research and development ofOFDM base-band system simulation platform and uplink channel failure analysisdetection system using neural network technology. The main innovation works are asfollows:
Access network technology and multi-carrier communication is the coretechnology for the next generation broadcast networks. Based on the analysis andstudy of the related access network technique protocol standard, this dissertation focuses on the characteristic function model including Gaussian noise, narrowbandcontinuous wave noise, pulse noise and BER expression, and the performance of nextgeneration cable television network based on the OFDM technique. UsingMatlab/Simulink software, we firstly constructed some modules such as the signalsource, modulation and demodulation, OFDM and BER calculation. Then base-bandOFDM systems simulation platform is formed using typical noise model andmodeling technology. Finally the simulation results are obtained by simulating theOFDM systems and analyzing the performance of communication system at theplatform. It lays solid foundation for further researching the next generation broadcastnetwork performance and developing special broadband network monitor system withindependent property.
Based on the above research results, BP neural network technique can be used inthe area of the next generation cable broadcast and TV network for upstream channelfault diagnosis. Spectrum characteristics of upstream channel are used as neuralnetwork inputs. Upstream channel spectrum data obtained by actual measurement isused to train the neural network so that noise and interference type can be obtained byanalyzing the neural network output. After a lot of simulations, the most suitablelearning algorithm and activation function of the BP neural network can be obtainedto be used for upstream channel fault diagnosis of the next generation cable broadcastand TV network. The corrections of the weights were also derived in this thesis basedon the most suitable algorithm and activation function.
Based on noise model expression derived by this thesis, upstream channel faultdiagnosis identification system for the next generation cable broadcast and TVnetwork can be built as well as cable broadcast and TV communication multi-carriersystem simulation platform. We designed a scheme of upstream channel noisemonitoring system for the next generation cable broadcast and TV network, which hasbeen embedded into the related products of the Deli Electronics Limited Company. Itplays a core technical role for the final product. Proved by actual application, theaccuracy of the fault diagnosis system based on BP neural network is more than85%.It verifies the correctness of the noise model, simulation platform and fault diagnosisalgorithm. We hope to contribute to the development of the next generation high-endbroadcast and TV network instrument for a special purpose with independentintellectual property rights.
引文
[1]中国下一代广播电视网（NGB）自主创新战略研究报告，NGB总体专家委员会，2010，6
[2]田伟莉，基于三网融合的广电信息化管理，中国管理信息化，2010，13(22)：58~59
[3]中国信息产业商会数据广播分会，中国地面无线广播电视发展现状及未来趋势探讨，广播电视信息，2011，(5)：23~26
[4]郭世满，马蕴颖，郭苏宁，宽带接入技术及应用，北京：北京邮电大学出版社，2006：1~17
[5]格兰研究，2010年度数字电视设备市场盘点，世界宽带网络，2011，18(1)：64~65
[6]新媒体产业发展研究所，中国数字电视发展现状研究，卫视传媒，2010，12，36~39
[7]宋健，中国地面数字电视发展现状与前景的思考，世界广播电视，2009，23(5)：83~85
[8]王丽娟，浅谈双向交互式有线电视技术的发展，中国科技博览，2010，(21)：85~85
[9]李俊，863重大专项3TNet介绍，合肥：中国科学技术大学，2006
[10]盛志凡，中国下一代网络NGB [EB/OL].http://www.dvbcn.com/system/2009416/16112316878.htm
[11]下一代广播电视网（NGB）示范区实施总体方案，国家广播电影电视总局科技司，2009，5
[12]面向下一代广播电视网(NGB)电缆接入技术(EOC)需求白皮书，国家广播电影电视总局科技司，2009，4
[13]沈向军，有线电视科技领域“十一五”回顾与“十二五”展望——在国家广电总局科技委八届四次会议上有线专业委员会的专题报告，现代电视技术，2011，(1)：18~22
[14]焦李成，神经网络的应用与实现，西安：西安电子科技大学出版社，1996，37~46
[15]汪振兴，刘臣宇，李丽，等，基于改进BP神经网络的某型装备故障诊断专家系统，计算机与现代化，2010，(2)：200~203
[16]唐洪法，基于BP神经网络的网络故障诊断系统，硕士学位论文，南京理工大学，2005
[17]李士勇，模糊控制·神经控制和智能控制论，哈尔滨：哈尔滨工业大学出版社，1998，20~67
[18]杨迎化，唐大全，卢建华，神经网络在智能故障诊断技术中的应用及其发展趋势，测控技术学报，2003，22(9)：1~5
[19]胡守仁，神经网络应用技术，长沙：国防科技大学出版社，1993，130~180
[20]虞和济，陈长征，张省，基于神经网络的智能诊断，振动工程学报，2000，13(2)：202~209
[21]陈会莲，谷明月，池丹丹，等，神经网络专家系统在数控机床机械故障诊断中的应用，噪声与振动控制，2010，(1)：86~88
[22]陈祥光，裴旭东，人工神经网络技术及应用，北京：中国电力出版社，2003，6~8
[23]刘继，夏定纯，基于神经网络的故障诊断，黑龙江科技信息，2010，(25)：84
[24]张杰，高宪军，姚劲勃，等，基于神经网络与专家系统的故障诊断技术，吉林大学学报：信息科学版，2009，27(3)：320~323
[25]时圣革，常文兵，李祥臣，故障诊断专家系统及发展趋势，机械工程师，2006，(11)：26~28
[26]袁科新，刘枚，神经网络在故障诊断中的应用，山东商业职业技术学院学报，2006，6(4)：97~99
[27]梁艳春，聂义勇，从科学研究方法论看人工神经网络研究的发展，吉林大学学报：信息科学版，2002，20(1)：59~62
[28]韩立群，人工神经网络教程，北京：北京邮电大学出版社，2006：54~67
[29]许听，潘铭志，王晶禹，等，基于神经网络的故障诊断方法的研究，机械管理开发，2007，(2)：89~91
[30]任建平，现代数控机床故障诊断及维修，北京：国防工业出版社，2002，2~11
[31]金鑫，任献彬，周亮，智能故障诊断技术研究综述，国外电子测量技术，2009，28(7)：30~32
[32]周杰，宋哲存，机械系统状态监控与故障诊断综述，森林工程，2004，20(2)：29~30
[33]王清，潘宏侠，周传刚，机械故障诊断技术现状及趋势，机械管理开发，2005，(6)：49~51
[34]王奉涛，马孝江，邹岩琨，智能故障诊断技术综述，机床与液压，2003，(4)：6~8
[35]吕淑琴，曹林，王亚飞，等，CATV／HFC反向通道噪声监测和抑制系统的研究，北京信息科技大学学报，2009，24(3)：87~90
[36]戴卫平，李永宁，梁军，HFC网络反向噪声远程实时监测系统，广播与电视技术，2006，(9)：92~94
[37]高智，HFC双向网络上行通道的噪声分析与日常监测，中国有线电视，2007，(23)：2191~193
[38]刘文开，刘远航，有线广播数字电视技术，北京：人民邮电出版社，2003，5~9
[39]郑雯，翟希山，王志广，等，数字电视原理、传输与接收，北京：人民邮电出版社，2006，180~183
[40]黄俊，现代有线电视网络技术及应用，北京：机械工业出版社，2010，9~89
[41] EOC技术比较
[EB/OL].http//xuyuann.blog.163.com/blog/static/532179022009112354251373/
[42]罗文睿，有线宽带接入EOC与MoCA的比较，中国有线电视，2007，(24)：2298~2300
[43] H3C有源EoC技术对比
[EB/OL].http://www.dvbcn.com/2009-4/7-30622_2.html
[44]方祁磊，HomePNA技术在信息化建设中的应用途径，淮南职业技术学院学报，2004，4(1)：90~91
[45]王延涛，ASDL、HomePNA和LAN宽带接入技术的比较，上海：科技信息，2007，(10)：193~195
[46]冯瑞明，HomePlug AV电力线调制解调器的设计与实现，电子产品世界，2008，3：103~106
[47]田新成，刘献伟，黄霞，等，基于HomePlug AV标准的自适应电力线modem设计，电力系统通信，2007，28(182)：62~65
[48] WLAN完全指南及无网关回应解决方案
[EB/OL].http://bbs.imobile.com.cn/thread-tid-8171013.html
[49] EOC[EB/OL]. http://baike.baidu.com/view/661238.htm
[50]第三代有线数据传输系统DOCSIS3.0[EB/OL].http://www.crta.com.cn/magazine/404/2010311113629.htm
[51]当前主流宽带接入技术介绍[EB/OL].http://www.3671041.com/Article/lan/adsl/200709/7261.html
[52]曾元，HFC网络上行信道噪声特性研究及建模，硕士学位论文，天津大学，2010
[53]王丹，基于统计理论的有线宽带接入网信道噪声模型研究，硕士学位论文，天津大学，2011
[54]王小平，王彦芳，陈东阳，等，OFDM技术在馈线自动化有线通信中的应用，继电器，2005，33(18)：57~60
[55]别志松，别红霞，信息与通信系统仿真，北京：北京邮电大学出版社，2010，9~100
[56]罗仁泽，张光玉，李杰，等，OFDM系统叠加序列能量分配及同步方案，电子学报，2006，34(4)：670~673
[57]正交频分复用
[EB/OL].http://wenku.baidu.com/view/1e31fed084254b35eefd3445.html
[58]梁芝贤，穆国强，电力系统通信规划实施与建议，光通信技术，2007，31(2)：25~27
[59]郝毅，IEEE+80211A+WLAN系统MATLAB仿真实现及其同步问题研究，硕士学位论文，电子科技大学，2006
[60]马满仓，杨杰，短波通信中的正交频分复用技术分析及应用，军民两用技术与产品，2006，(10)：36~37
[61]啜钢，王文博，常永宇，等，移动通信原理与系统，北京：北京邮电大学出版社，2006，459~465
[62]邵佳，董辰辉，Matlab/Simulink通信系统建模与仿真实例精讲，北京：电子工业出版社，2009，7~39
[63]支玉亮，OFDM系统的同步技术研究，硕士学位论文，天津大学，2010
[64]周恩，张兴，吕召彪，等，下一代宽带无线通信OFDM与MIMO技术，北京：人民邮电出版社，2008，3~4，22~26
[65] Q.J. Zhang and Y. Cao, Time-domain neural network approaches to EMmodeling of microwave components, Time Domain Methods inElectrodynamics, Edited by P. Russer, and U. Siart, New York: Springer,2008:41~54
[66] Q.J. Zhang, M. Deo and J.J. Xu, Neural networks for microwave circuits,Encyclopedia of RF and Microwave Engineering, Editor in Chief, K. Chang,New York: Wiley,2005.(invited)
[67] Q.J. Zhang and J.J. Xu, Artificial neural networks for behavioral modeling, inFundamentals of Nonlinear Behavioral Modeling for RF and MicrowaveDesigns, Edited by J. Wood and D. Root, Boston, MA: Artech House, Chapter6,2005.(invited)
[68] Chen Zhihang (IEEE), Ni Chengwen, Murphey, Neural network approachesfor text document categorization, IEEE International Conference on NeuralNetworks-Conference Proceedings, International Joint Conference on NeuralNetworks2006, IJCNN'06,2006:1054~1060
[69] Lee Kwang Y (IEEE), System-type neural network architectures for powersystems, IEEE International Conference on Neural Networks-ConferenceProceedings, International Joint Conference on Neural Networks2006, IJCNN'06,2006:1702~1709
[70] Chen Dingding (IEEE), Quirein John, Smith Harry, Variable input neuralnetwork ensembles in generating synthetic well logs, IEEE InternationalConference on Neural Networks-Conference Proceedings, International JointConference on Neural Networks2006, IJCNN '06,2006:1294~1301
[71] Zhao Liang(IEEE), Wang Fei-Yue, Short-term fuzzy traffic flow predictionusing self-organizing TSK-type fuzzy neural network,2007IEEE InternationalConference on Vehicular Electronics and Safety, ICVES,2007
[72] Ghiassi M., Zimbra David K., Saidane H., Medium term system loadforecasting with a dynamic artificial neural network model, Electric PowerSystems Research, March2006,76(5):302~316
[73] Choi Tae-Il (IEEE), Lee Kwang Y., Junker S. Tobias, Neural networksupervisor for hybrid fuel cell/gas turbine power plants,2007IEEE PowerEngineering Society General Meeting, PES,2007
[74]神经网络用于故障诊断的实现方法[EB/OL].http://www.chinabaike.com/z/keji/jixie/2011/0418/841248.html
[75] Dong Puxuan (IEEE), Bilbro Griff L., Chow, Mo-Yuen, Implementation ofartificial neural network for real time applications using field programmableanalog arrays, IEEE International Conference on Neural Networks-Conference Proceedings, International Joint Conference on Neural Networks2006, IJCNN '06,2006:1518~1524
[76] Tao Cheng(IEEE), Lewis Frank L, Fixed-final time constrained optimal controlof nonlinear systems using neural network HJB approach, Proceedings of theIEEE Conference on Decision and Control, Proceedings of the45th IEEEConference on Decision and Control2006, CDC,2006:3016~3021
[77]张立明，人工神经网络的模型及其应用，上海：复旦大学出版社，1994，32~64
[78]朱大奇，史慧，人工神经网络原理及应用，北京：科学出版社，2006，33~62
[79] Hobbs Nathaniel J(IEEE), Kim Byoung H., Lee Kwang Y., Long-term loadforecasting using system type neural network architecture,2007InternationalConference on Intelligent Systems Applications to Power Systems, ISAP,2007
[80] Q.J. Zhang and K.C. Gupta, Application of Artificial Neural Networks to RFand Microwave Design, A Short Course in IEEE MTT-S InternationalMicrowave Symposium (Honolulu, Hawaii), June2007(short courseOrganizers and Instructors)
[81] Xuemei Ren(IEEE), Rad A. B., Lewis Frank L., Neural network-basedcompensation control of robot manipulators with unknown dynamics,Proceedings of the American Control Conference, ACC2007:13~18
[82] Q.J. Zhang, Neural based techniques for microwave modeling, Workshop onMicowaves, Communication and Signal Processing,(Nanjing, China), May2007(invited)
[83]何沿江，基于声发射技术的滚动轴承故障诊断研究，硕士学位论文，中国石油大学(华东)，2008
[84] Cheng Tao (IEEE), Lewis Frank L., Abu-Khalaf Murad, A neural networksolution for fixed-final time optimal control of nonlinear systems,14thMediterranean Conference on Control and Automation, MED2006
[85] Aizenberg Igor(IEEE), Paliy Dmitriy, Astola Jaakko T., Multilayer neuralnetwork based on multi-valued neurons and the Blur identification problem,IEEE International Conference on Neural Networks-Conference Proceedings,International Joint Conference on Neural Networks2006, IJCNN '06,2006:473~480
[86]丛爽，面向MATLAB工具箱的神经网络理论与应用，合肥：中国科学技术大学出版社，1998：20~27
[87]兰雪梅，朱健，BP网络的MATLAB的实现，微型电脑应用，2003，19(1)：6~8
[88]高兴培，用MATLAB语言仿真设计人工神经网络，广东广播电视大学学报，2004，13(1)：45~47
[89]王旭，王宏，人工神经元网络原理与应用，沈阳：东北大学出版社，2000，51~59
[90] H. Kabir, L. Zhang, M. Yu, P. Aaen, J. Wood and Q.J. Zhang, Neural networksfor microwave modeling and design, IEEE Microwave Magazine, May2010,11(3):105~118
[91] Kamalasadan S.(IEEE), Srivastava A. K., Thukaram D., Novel algorithm foronline voltage stability assessment based on feed forward neural network,2006IEEE Power Engineering Society General Meeting, PES,2006
[92] Shaft Imran (IEEE), Ahmad Jamil, Shah Syed Ismail, Impact of varyingneurons and hidden layers in neural network architecture for a time frequencyapplication,10th IEEE International Multitopic Conference2006, INMIC,2006:188~193
[93] Xiong N.(IEEE), Yang Y., He Jing, On designing QoS for congestion controlservice using neural network predictive techniques,2006IEEE InternationalConference on Granular Computing,2006:299~304
[94] Ma Liangyu (IEEE), Lee Kwang Y., Fuzzy neural network approach for faultdiagnosis of power plant thermal system under different operating points, IEEEPower and Energy Society2008General Meeting: Conversion and Delivery ofElectrical Energy in the21st Century, PES,2008
[95] M.Simsek, Q.J. Zhang, H. Kabir, Y. Cao and N. Sengor, The recentdevelopments in knowledge based neural mdoeling, International Conferenceon Computational Science (Amsterdam, the Netherlands), May-June,2010
[96] Q.J. Zhang, Parametric time-domain neural network models for microwavemodeling, Progress in Electromagnetics Research Symposium (Xian, China)March2010
[97] Hou Yanfeng (IEEE), Zurada Jacek M., Karwowski Waldemar, Identificationof low back injury from EMG signals using a neural network model, IEEEInternational Conference on Neural Networks-Conference Proceedings,International Joint Conference on Neural Networks2006, IJCNN’06,2006:5308~5315
[98] Slaughter Gymama E.(IEEE), Hobson Rosalyn S., Artificial neural networkfor temporal impedance recognition of neurotoxins, IEEE InternationalConference on Neural Networks-Conference Proceedings, InternationalJoint Conference on Neural Networks2006, IJCNN '06,2006:2001~2008
[99] Pang Zhongyu (IEEE), Liu Derong, Jin Ning, Neural network strategy forsampling of particle filters on the tracking problem, IEEE InternationalConference on Neural Networks-Conference Proceedings, The2007International Joint Conference on Neural Networks, IJCNN2007ConferenceProceedings,2007:254~259
[100] Mazurowski Maciej A.(IEEE), Habas Piotr A., Zurada Jacek M., Impact oflow class prevalence on the performance evaluation of neural network basedclassifiers: Experimental study in the context of computer-assisted medicaldiagnosis, IEEE International Conference on Neural Networks-ConferenceProceedings, The2007International Joint Conference on Neural Networks,IJCNN2007Conference Proceedings,2007:2005~2009
[101] Gao Robert X(IEEE), Wang Changting, Yan Ruqiang, A neural networkapproach to bearing health assessment, IEEE International Conference onNeural Networks-Conference Proceedings, International Joint Conference onNeural Networks2006, IJCNN '062006:899~906
[102] H. Kabir, Y. Wang, M. Yu and Q.J. Zhang, Neural network inverse modelingmethods for fast and accurate solution of electromagnetic devices, ACES Conf.Applied Computational Electromagnetics,(Monterey, California), March2009
[103] Dang Pritpal(IEEE), Stephanou Harry, Ham Fredric, Facial expressionrecognition using a two stage neural network,2007Mediterranean Conferenceon Control and Automation, MED2007
[104] H. Ninomiya and Q.J. Zhang, Particle with Ability of Local search SwarmOptimization: PALSO for Training of Feedforward Neural Networks, IEEE Int.Joint Conf. on Neural Networks,(Hong Kong), May2008
[105] Q.J. Zhang, ANN/DNN-based behavioural modeling of RF/microwavecomponents/devices and circuit blocks, Int. Conf. on Scientific Computing inElectrical Engineering (Helsinki, Finland) Sept2008.(invited)
[106]苏高利，邓芳萍，论基于MATLAB语言的BP神经网络的改进算法，科技通报，2003，19(2)：130~135
[107]刘宝光，非线性规划，北京：北京理工大学出版社，1988，119~132
[108] Y. Z. Cao, G.F. Wang, and Q.J. Zhang, A new training approach for parametricmodeling of microwave passive components using combined neural networksand transfer functions, IEEE Trans. Microwave Theory Tech., November,2009,57(11):2727~2742
[109] Barmpoutis Angelos (IEEE), Ritter Gerhard X., Orthonormal basis latticeneural networks, IEEE International Conference on Fuzzy Systems,2006IEEEInternational Conference on Fuzzy Systems,2006:331~336
[110] Li Hui (IEEE), Zhang Da, Foo Simon Y., A stochastic digital implementationof a neural network controller for small wind turbine systems, IEEETransactions on Power Electronics,2006,21(5):1502~1507
[111] X.P. Li, J.J. Gao, and Q.J. Zhang, Microwave noise modeling for PHEMTusing artificial neural network technique, Int. J. RF and Microwave CAE,March2009,19(2):187~196
[112] Dibazar Alireza A.(IEEE), Park Hyung O., Berger Theodore W., Theapplication of dynamic synapse neural networks on footstep and vehiclerecognition, IEEE International Conference on Neural Networks-ConferenceProceedings, The2007International Joint Conference on Neural Networks,IJCNN2007Conference Proceedings,2007:1842~1846
[113] H. Kabir, Y. Wang, M. Yu, and Q.J. Zhang, Neural network inverse modelingand applications to microwave filter design, IEEE Trans. Microwave TheoryTech., vol.56, no.4, pp.867~879, April2008
[114] Palangpour Parviz (IEEE), Venayagamoorthy Ganesh K., Duffy, Kevin,Recurrent neural network based predictions of elephant migration in a SouthAfrican game reserve, IEEE International Conference on Neural Networks-Conference Proceedings, International Joint Conference on Neural Networks2006, IJCNN ‘06,2006:4084~4088
[115] Zadeh H. Khorashadi (IEEE), Li Z., Artificial neural network based loadblinder for distance protection, IEEE Power and Energy Society2008GeneralMeeting: Conversion and Delivery of Electrical Energy in the21st Century,PES,2008
[116] Yadav Vivek (IEEE), Wang Xiaohua, Balakrishnan S. N., Neural networkapproach for obstacle avoidance in3-D environments for UAVs, Proceedingsof the American Control Conference,2006:3667~3672
[117] Y. Cao, J.J. Xu, V.K. Devabhaktuni, R.T. Ding, and Q.J. Zhang, An adjointsensitivity technique for dynamic neural network modeling and design ofhigh-speed interconnect, International Journal on RF and Microwave CAE,2006,16(4):385~399
[118] Jin Ning (IEEE), Liu Derong, Pang Zhongyu, Wavelet basis function neuralnetworks, IEEE International Conference on Neural Networks-ConferenceProceedings, The2007International Joint Conference on Neural Networks,IJCNN2007Conference Proceedings,2007:500~505
[119] Park Hyung O.(IEEE), Dibazar Alireza A., Berger Theodore W, Protectingmilitary perimeters from approaching human and vehicle using biologicallyrealistic dynamic synapse neural network,2008IEEE International Conferenceon Technologies for Homeland Security, HST'08,2008:73~78
[120] Y. Cao and Q.J. Zhang, Adjoint dynamic neural network for developingtransient nonlinear neural models and sensitivity analysis in high-speed circuitdesign, Annual Review of Progress in Applied ComputationalElectromagnetics,(Niagara Falls, Canada), March2008
[121] Chen Tianping (IEEE), Bai Yanchun, Dankert James, Stability ofCohen-Grossberg neural networks with nonnegative periodic solutions, IEEEInternational Conference on Neural Networks-Conference Proceedings, The2007International Joint Conference on Neural Networks,, IJCNN2007Conference Proceedings,2007:242~247
[122] S. Wan, L. Zhang and Q.J. Zhang, Application of artificial neural networks forelectromagnetic modeling and computational electromagnetics, IEEE MidwestSymposium on Circuits and Systems (Knoxville, Tennessee) August2008
[123] Gebraeel Nagi Z (IEEE), Lawley Mark A., A neural network degradationmodel for computing and updating residual life distributions, IEEETransactions on Automation Science and Engineering, January2008,5(1):154~163
[124] H. Kabir, M. Yu, and Q.J. Zhang, State-of-the-art microwave filter modelingand design using neural network technique, Progress in ElectromagneticResearch Symposium (Hangzhou, China) March2008
[125]王光研，许宝杰，弹性BP神经网络的旋转机械故障诊断，北京：北京机械工业学院学报，2007，9~12
[126] Hong, Zhenya Ha Variable slap BP algorithm which prunes away redundantconnections dynamically Beijing: IJCNN,1992,2:441~445
[127] Zhang Y. D., Wu I. N., Weights Optimization of Neural Network via ImprovedBCO Approach[J], Progress in Electromagnetic Research,2008,83:185~198
[128] Constantinescu, Lazarescu, Tahboub, Geometrical form recognition using"one-step-secant" algorithm in case of neural network, University"Politehnica" of Bucharest, Scientific Bulletin Series C: Electrical Engineering,2008,70(2):15~28
[129] Rao H.I.K, Farhang-Boroujeny B., Analysis of the Stereophonic LMS/NewtonAlgorithm and Impact of Signal Nonlinearity on Its Convergence Behavior,IEEE Transactions on Signal Processing,2010,58(12):6080~6092
[130] Finsterle Stefan, Kowalsky Michael B., A truncated Levenberg-Marquardtalgorithm for the calibration of highly parameterized nonlinear models,Computers and Geosciences,2011,37(6):731~738
[131]田长国，有线电视双向HFC网络上行信道噪声分析，有线网络，2002，(3)：35~37
[132]王淦平，于凤芹，王灿忠，等，HFC反向通道噪声监测和抑制系统，电视技术，2007，31(5)：69~71
[133]陈韬， HFC反向通道噪声与干扰信号研究，电视技术，2008，(5)：52~54
[134]李慧，杨东梅，沈洁，等，BP神经网络在路径规划中的应用，应用科技，2004，31(9)：15~17
[135] YANG J. M., Sliding mode motion control of nonholonomic mobile, IEEEControl Systems,1999,(1):15~23
[136] Chun-Yi, Robust motion/forcecontrol of mechanical systerms with classicalnonholonomic constraints, IEEE Automation Control,1998,39(3):0~61
[137] YAMAMOTO M., USHIMI N., MOHRI A., Sensor-based navigation formobile robots using taget direction sensor, Jourual of the Rohoties Society ofJapan,1995,13(8):l130~l137
[138]冯居易，郭晔，基于LM算法的石油期货价格预测研究，技术经济与管理研究，2009，(5)：19~21
[139] Moller M. F. A, scaled conjugate gradient algorithm for eficient training ofartificial neural Networks,1992,(6):301~310
[140] Deepak Mishra, Abhishek Yadav, Sudipta Ray, and Prem K. Kalra,Levenberg-Marquardt Learning Algorithm for Integrate and FireNeuronModel[J], Neural Information Processing—Letters and Reviews,2005,9
[2]田伟莉，基于三网融合的广电信息化管理，中国管理信息化，2010，13(22)：58~59
[3]中国信息产业商会数据广播分会，中国地面无线广播电视发展现状及未来趋势探讨，广播电视信息，2011，(5)：23~26
[4]郭世满，马蕴颖，郭苏宁，宽带接入技术及应用，北京：北京邮电大学出版社，2006：1~17
[5]格兰研究，2010年度数字电视设备市场盘点，世界宽带网络，2011，18(1)：64~65
[6]新媒体产业发展研究所，中国数字电视发展现状研究，卫视传媒，2010，12，36~39
[7]宋健，中国地面数字电视发展现状与前景的思考，世界广播电视，2009，23(5)：83~85
[8]王丽娟，浅谈双向交互式有线电视技术的发展，中国科技博览，2010，(21)：85~85
[9]李俊，863重大专项3TNet介绍，合肥：中国科学技术大学，2006
[10]盛志凡，中国下一代网络NGB [EB/OL].http://www.dvbcn.com/system/2009416/16112316878.htm
[11]下一代广播电视网（NGB）示范区实施总体方案，国家广播电影电视总局科技司，2009，5
[12]面向下一代广播电视网(NGB)电缆接入技术(EOC)需求白皮书，国家广播电影电视总局科技司，2009，4
[13]沈向军，有线电视科技领域“十一五”回顾与“十二五”展望——在国家广电总局科技委八届四次会议上有线专业委员会的专题报告，现代电视技术，2011，(1)：18~22
[14]焦李成，神经网络的应用与实现，西安：西安电子科技大学出版社，1996，37~46
[15]汪振兴，刘臣宇，李丽，等，基于改进BP神经网络的某型装备故障诊断专家系统，计算机与现代化，2010，(2)：200~203
[16]唐洪法，基于BP神经网络的网络故障诊断系统，硕士学位论文，南京理工大学，2005
[17]李士勇，模糊控制·神经控制和智能控制论，哈尔滨：哈尔滨工业大学出版社，1998，20~67
[18]杨迎化，唐大全，卢建华，神经网络在智能故障诊断技术中的应用及其发展趋势，测控技术学报，2003，22(9)：1~5
[19]胡守仁，神经网络应用技术，长沙：国防科技大学出版社，1993，130~180
[20]虞和济，陈长征，张省，基于神经网络的智能诊断，振动工程学报，2000，13(2)：202~209
[21]陈会莲，谷明月，池丹丹，等，神经网络专家系统在数控机床机械故障诊断中的应用，噪声与振动控制，2010，(1)：86~88
[22]陈祥光，裴旭东，人工神经网络技术及应用，北京：中国电力出版社，2003，6~8
[23]刘继，夏定纯，基于神经网络的故障诊断，黑龙江科技信息，2010，(25)：84
[24]张杰，高宪军，姚劲勃，等，基于神经网络与专家系统的故障诊断技术，吉林大学学报：信息科学版，2009，27(3)：320~323
[25]时圣革，常文兵，李祥臣，故障诊断专家系统及发展趋势，机械工程师，2006，(11)：26~28
[26]袁科新，刘枚，神经网络在故障诊断中的应用，山东商业职业技术学院学报，2006，6(4)：97~99
[27]梁艳春，聂义勇，从科学研究方法论看人工神经网络研究的发展，吉林大学学报：信息科学版，2002，20(1)：59~62
[28]韩立群，人工神经网络教程，北京：北京邮电大学出版社，2006：54~67
[29]许听，潘铭志，王晶禹，等，基于神经网络的故障诊断方法的研究，机械管理开发，2007，(2)：89~91
[30]任建平，现代数控机床故障诊断及维修，北京：国防工业出版社，2002，2~11
[31]金鑫，任献彬，周亮，智能故障诊断技术研究综述，国外电子测量技术，2009，28(7)：30~32
[32]周杰，宋哲存，机械系统状态监控与故障诊断综述，森林工程，2004，20(2)：29~30
[33]王清，潘宏侠，周传刚，机械故障诊断技术现状及趋势，机械管理开发，2005，(6)：49~51
[34]王奉涛，马孝江，邹岩琨，智能故障诊断技术综述，机床与液压，2003，(4)：6~8
[35]吕淑琴，曹林，王亚飞，等，CATV／HFC反向通道噪声监测和抑制系统的研究，北京信息科技大学学报，2009，24(3)：87~90
[36]戴卫平，李永宁，梁军，HFC网络反向噪声远程实时监测系统，广播与电视技术，2006，(9)：92~94
[37]高智，HFC双向网络上行通道的噪声分析与日常监测，中国有线电视，2007，(23)：2191~193
[38]刘文开，刘远航，有线广播数字电视技术，北京：人民邮电出版社，2003，5~9
[39]郑雯，翟希山，王志广，等，数字电视原理、传输与接收，北京：人民邮电出版社，2006，180~183
[40]黄俊，现代有线电视网络技术及应用，北京：机械工业出版社，2010，9~89
[41] EOC技术比较
[EB/OL].http//xuyuann.blog.163.com/blog/static/532179022009112354251373/
[42]罗文睿，有线宽带接入EOC与MoCA的比较，中国有线电视，2007，(24)：2298~2300
[43] H3C有源EoC技术对比
[EB/OL].http://www.dvbcn.com/2009-4/7-30622_2.html
[44]方祁磊，HomePNA技术在信息化建设中的应用途径，淮南职业技术学院学报，2004，4(1)：90~91
[45]王延涛，ASDL、HomePNA和LAN宽带接入技术的比较，上海：科技信息，2007，(10)：193~195
[46]冯瑞明，HomePlug AV电力线调制解调器的设计与实现，电子产品世界，2008，3：103~106
[47]田新成，刘献伟，黄霞，等，基于HomePlug AV标准的自适应电力线modem设计，电力系统通信，2007，28(182)：62~65
[48] WLAN完全指南及无网关回应解决方案
[EB/OL].http://bbs.imobile.com.cn/thread-tid-8171013.html
[49] EOC[EB/OL]. http://baike.baidu.com/view/661238.htm
[50]第三代有线数据传输系统DOCSIS3.0[EB/OL].http://www.crta.com.cn/magazine/404/2010311113629.htm
[51]当前主流宽带接入技术介绍[EB/OL].http://www.3671041.com/Article/lan/adsl/200709/7261.html
[52]曾元，HFC网络上行信道噪声特性研究及建模，硕士学位论文，天津大学，2010
[53]王丹，基于统计理论的有线宽带接入网信道噪声模型研究，硕士学位论文，天津大学，2011
[54]王小平，王彦芳，陈东阳，等，OFDM技术在馈线自动化有线通信中的应用，继电器，2005，33(18)：57~60
[55]别志松，别红霞，信息与通信系统仿真，北京：北京邮电大学出版社，2010，9~100
[56]罗仁泽，张光玉，李杰，等，OFDM系统叠加序列能量分配及同步方案，电子学报，2006，34(4)：670~673
[57]正交频分复用
[EB/OL].http://wenku.baidu.com/view/1e31fed084254b35eefd3445.html
[58]梁芝贤，穆国强，电力系统通信规划实施与建议，光通信技术，2007，31(2)：25~27
[59]郝毅，IEEE+80211A+WLAN系统MATLAB仿真实现及其同步问题研究，硕士学位论文，电子科技大学，2006
[60]马满仓，杨杰，短波通信中的正交频分复用技术分析及应用，军民两用技术与产品，2006，(10)：36~37
[61]啜钢，王文博，常永宇，等，移动通信原理与系统，北京：北京邮电大学出版社，2006，459~465
[62]邵佳，董辰辉，Matlab/Simulink通信系统建模与仿真实例精讲，北京：电子工业出版社，2009，7~39
[63]支玉亮，OFDM系统的同步技术研究，硕士学位论文，天津大学，2010
[64]周恩，张兴，吕召彪，等，下一代宽带无线通信OFDM与MIMO技术，北京：人民邮电出版社，2008，3~4，22~26
[65] Q.J. Zhang and Y. Cao, Time-domain neural network approaches to EMmodeling of microwave components, Time Domain Methods inElectrodynamics, Edited by P. Russer, and U. Siart, New York: Springer,2008:41~54
[66] Q.J. Zhang, M. Deo and J.J. Xu, Neural networks for microwave circuits,Encyclopedia of RF and Microwave Engineering, Editor in Chief, K. Chang,New York: Wiley,2005.(invited)
[67] Q.J. Zhang and J.J. Xu, Artificial neural networks for behavioral modeling, inFundamentals of Nonlinear Behavioral Modeling for RF and MicrowaveDesigns, Edited by J. Wood and D. Root, Boston, MA: Artech House, Chapter6,2005.(invited)
[68] Chen Zhihang (IEEE), Ni Chengwen, Murphey, Neural network approachesfor text document categorization, IEEE International Conference on NeuralNetworks-Conference Proceedings, International Joint Conference on NeuralNetworks2006, IJCNN'06,2006:1054~1060
[69] Lee Kwang Y (IEEE), System-type neural network architectures for powersystems, IEEE International Conference on Neural Networks-ConferenceProceedings, International Joint Conference on Neural Networks2006, IJCNN'06,2006:1702~1709
[70] Chen Dingding (IEEE), Quirein John, Smith Harry, Variable input neuralnetwork ensembles in generating synthetic well logs, IEEE InternationalConference on Neural Networks-Conference Proceedings, International JointConference on Neural Networks2006, IJCNN '06,2006:1294~1301
[71] Zhao Liang(IEEE), Wang Fei-Yue, Short-term fuzzy traffic flow predictionusing self-organizing TSK-type fuzzy neural network,2007IEEE InternationalConference on Vehicular Electronics and Safety, ICVES,2007
[72] Ghiassi M., Zimbra David K., Saidane H., Medium term system loadforecasting with a dynamic artificial neural network model, Electric PowerSystems Research, March2006,76(5):302~316
[73] Choi Tae-Il (IEEE), Lee Kwang Y., Junker S. Tobias, Neural networksupervisor for hybrid fuel cell/gas turbine power plants,2007IEEE PowerEngineering Society General Meeting, PES,2007
[74]神经网络用于故障诊断的实现方法[EB/OL].http://www.chinabaike.com/z/keji/jixie/2011/0418/841248.html
[75] Dong Puxuan (IEEE), Bilbro Griff L., Chow, Mo-Yuen, Implementation ofartificial neural network for real time applications using field programmableanalog arrays, IEEE International Conference on Neural Networks-Conference Proceedings, International Joint Conference on Neural Networks2006, IJCNN '06,2006:1518~1524
[76] Tao Cheng(IEEE), Lewis Frank L, Fixed-final time constrained optimal controlof nonlinear systems using neural network HJB approach, Proceedings of theIEEE Conference on Decision and Control, Proceedings of the45th IEEEConference on Decision and Control2006, CDC,2006:3016~3021
[77]张立明，人工神经网络的模型及其应用，上海：复旦大学出版社，1994，32~64
[78]朱大奇，史慧，人工神经网络原理及应用，北京：科学出版社，2006，33~62
[79] Hobbs Nathaniel J(IEEE), Kim Byoung H., Lee Kwang Y., Long-term loadforecasting using system type neural network architecture,2007InternationalConference on Intelligent Systems Applications to Power Systems, ISAP,2007
[80] Q.J. Zhang and K.C. Gupta, Application of Artificial Neural Networks to RFand Microwave Design, A Short Course in IEEE MTT-S InternationalMicrowave Symposium (Honolulu, Hawaii), June2007(short courseOrganizers and Instructors)
[81] Xuemei Ren(IEEE), Rad A. B., Lewis Frank L., Neural network-basedcompensation control of robot manipulators with unknown dynamics,Proceedings of the American Control Conference, ACC2007:13~18
[82] Q.J. Zhang, Neural based techniques for microwave modeling, Workshop onMicowaves, Communication and Signal Processing,(Nanjing, China), May2007(invited)
[83]何沿江，基于声发射技术的滚动轴承故障诊断研究，硕士学位论文，中国石油大学(华东)，2008
[84] Cheng Tao (IEEE), Lewis Frank L., Abu-Khalaf Murad, A neural networksolution for fixed-final time optimal control of nonlinear systems,14thMediterranean Conference on Control and Automation, MED2006
[85] Aizenberg Igor(IEEE), Paliy Dmitriy, Astola Jaakko T., Multilayer neuralnetwork based on multi-valued neurons and the Blur identification problem,IEEE International Conference on Neural Networks-Conference Proceedings,International Joint Conference on Neural Networks2006, IJCNN '06,2006:473~480
[86]丛爽，面向MATLAB工具箱的神经网络理论与应用，合肥：中国科学技术大学出版社，1998：20~27
[87]兰雪梅，朱健，BP网络的MATLAB的实现，微型电脑应用，2003，19(1)：6~8
[88]高兴培，用MATLAB语言仿真设计人工神经网络，广东广播电视大学学报，2004，13(1)：45~47
[89]王旭，王宏，人工神经元网络原理与应用，沈阳：东北大学出版社，2000，51~59
[90] H. Kabir, L. Zhang, M. Yu, P. Aaen, J. Wood and Q.J. Zhang, Neural networksfor microwave modeling and design, IEEE Microwave Magazine, May2010,11(3):105~118
[91] Kamalasadan S.(IEEE), Srivastava A. K., Thukaram D., Novel algorithm foronline voltage stability assessment based on feed forward neural network,2006IEEE Power Engineering Society General Meeting, PES,2006
[92] Shaft Imran (IEEE), Ahmad Jamil, Shah Syed Ismail, Impact of varyingneurons and hidden layers in neural network architecture for a time frequencyapplication,10th IEEE International Multitopic Conference2006, INMIC,2006:188~193
[93] Xiong N.(IEEE), Yang Y., He Jing, On designing QoS for congestion controlservice using neural network predictive techniques,2006IEEE InternationalConference on Granular Computing,2006:299~304
[94] Ma Liangyu (IEEE), Lee Kwang Y., Fuzzy neural network approach for faultdiagnosis of power plant thermal system under different operating points, IEEEPower and Energy Society2008General Meeting: Conversion and Delivery ofElectrical Energy in the21st Century, PES,2008
[95] M.Simsek, Q.J. Zhang, H. Kabir, Y. Cao and N. Sengor, The recentdevelopments in knowledge based neural mdoeling, International Conferenceon Computational Science (Amsterdam, the Netherlands), May-June,2010
[96] Q.J. Zhang, Parametric time-domain neural network models for microwavemodeling, Progress in Electromagnetics Research Symposium (Xian, China)March2010
[97] Hou Yanfeng (IEEE), Zurada Jacek M., Karwowski Waldemar, Identificationof low back injury from EMG signals using a neural network model, IEEEInternational Conference on Neural Networks-Conference Proceedings,International Joint Conference on Neural Networks2006, IJCNN’06,2006:5308~5315
[98] Slaughter Gymama E.(IEEE), Hobson Rosalyn S., Artificial neural networkfor temporal impedance recognition of neurotoxins, IEEE InternationalConference on Neural Networks-Conference Proceedings, InternationalJoint Conference on Neural Networks2006, IJCNN '06,2006:2001~2008
[99] Pang Zhongyu (IEEE), Liu Derong, Jin Ning, Neural network strategy forsampling of particle filters on the tracking problem, IEEE InternationalConference on Neural Networks-Conference Proceedings, The2007International Joint Conference on Neural Networks, IJCNN2007ConferenceProceedings,2007:254~259
[100] Mazurowski Maciej A.(IEEE), Habas Piotr A., Zurada Jacek M., Impact oflow class prevalence on the performance evaluation of neural network basedclassifiers: Experimental study in the context of computer-assisted medicaldiagnosis, IEEE International Conference on Neural Networks-ConferenceProceedings, The2007International Joint Conference on Neural Networks,IJCNN2007Conference Proceedings,2007:2005~2009
[101] Gao Robert X(IEEE), Wang Changting, Yan Ruqiang, A neural networkapproach to bearing health assessment, IEEE International Conference onNeural Networks-Conference Proceedings, International Joint Conference onNeural Networks2006, IJCNN '062006:899~906
[102] H. Kabir, Y. Wang, M. Yu and Q.J. Zhang, Neural network inverse modelingmethods for fast and accurate solution of electromagnetic devices, ACES Conf.Applied Computational Electromagnetics,(Monterey, California), March2009
[103] Dang Pritpal(IEEE), Stephanou Harry, Ham Fredric, Facial expressionrecognition using a two stage neural network,2007Mediterranean Conferenceon Control and Automation, MED2007
[104] H. Ninomiya and Q.J. Zhang, Particle with Ability of Local search SwarmOptimization: PALSO for Training of Feedforward Neural Networks, IEEE Int.Joint Conf. on Neural Networks,(Hong Kong), May2008
[105] Q.J. Zhang, ANN/DNN-based behavioural modeling of RF/microwavecomponents/devices and circuit blocks, Int. Conf. on Scientific Computing inElectrical Engineering (Helsinki, Finland) Sept2008.(invited)
[106]苏高利，邓芳萍，论基于MATLAB语言的BP神经网络的改进算法，科技通报，2003，19(2)：130~135
[107]刘宝光，非线性规划，北京：北京理工大学出版社，1988，119~132
[108] Y. Z. Cao, G.F. Wang, and Q.J. Zhang, A new training approach for parametricmodeling of microwave passive components using combined neural networksand transfer functions, IEEE Trans. Microwave Theory Tech., November,2009,57(11):2727~2742
[109] Barmpoutis Angelos (IEEE), Ritter Gerhard X., Orthonormal basis latticeneural networks, IEEE International Conference on Fuzzy Systems,2006IEEEInternational Conference on Fuzzy Systems,2006:331~336
[110] Li Hui (IEEE), Zhang Da, Foo Simon Y., A stochastic digital implementationof a neural network controller for small wind turbine systems, IEEETransactions on Power Electronics,2006,21(5):1502~1507
[111] X.P. Li, J.J. Gao, and Q.J. Zhang, Microwave noise modeling for PHEMTusing artificial neural network technique, Int. J. RF and Microwave CAE,March2009,19(2):187~196
[112] Dibazar Alireza A.(IEEE), Park Hyung O., Berger Theodore W., Theapplication of dynamic synapse neural networks on footstep and vehiclerecognition, IEEE International Conference on Neural Networks-ConferenceProceedings, The2007International Joint Conference on Neural Networks,IJCNN2007Conference Proceedings,2007:1842~1846
[113] H. Kabir, Y. Wang, M. Yu, and Q.J. Zhang, Neural network inverse modelingand applications to microwave filter design, IEEE Trans. Microwave TheoryTech., vol.56, no.4, pp.867~879, April2008
[114] Palangpour Parviz (IEEE), Venayagamoorthy Ganesh K., Duffy, Kevin,Recurrent neural network based predictions of elephant migration in a SouthAfrican game reserve, IEEE International Conference on Neural Networks-Conference Proceedings, International Joint Conference on Neural Networks2006, IJCNN ‘06,2006:4084~4088
[115] Zadeh H. Khorashadi (IEEE), Li Z., Artificial neural network based loadblinder for distance protection, IEEE Power and Energy Society2008GeneralMeeting: Conversion and Delivery of Electrical Energy in the21st Century,PES,2008
[116] Yadav Vivek (IEEE), Wang Xiaohua, Balakrishnan S. N., Neural networkapproach for obstacle avoidance in3-D environments for UAVs, Proceedingsof the American Control Conference,2006:3667~3672
[117] Y. Cao, J.J. Xu, V.K. Devabhaktuni, R.T. Ding, and Q.J. Zhang, An adjointsensitivity technique for dynamic neural network modeling and design ofhigh-speed interconnect, International Journal on RF and Microwave CAE,2006,16(4):385~399
[118] Jin Ning (IEEE), Liu Derong, Pang Zhongyu, Wavelet basis function neuralnetworks, IEEE International Conference on Neural Networks-ConferenceProceedings, The2007International Joint Conference on Neural Networks,IJCNN2007Conference Proceedings,2007:500~505
[119] Park Hyung O.(IEEE), Dibazar Alireza A., Berger Theodore W, Protectingmilitary perimeters from approaching human and vehicle using biologicallyrealistic dynamic synapse neural network,2008IEEE International Conferenceon Technologies for Homeland Security, HST'08,2008:73~78
[120] Y. Cao and Q.J. Zhang, Adjoint dynamic neural network for developingtransient nonlinear neural models and sensitivity analysis in high-speed circuitdesign, Annual Review of Progress in Applied ComputationalElectromagnetics,(Niagara Falls, Canada), March2008
[121] Chen Tianping (IEEE), Bai Yanchun, Dankert James, Stability ofCohen-Grossberg neural networks with nonnegative periodic solutions, IEEEInternational Conference on Neural Networks-Conference Proceedings, The2007International Joint Conference on Neural Networks,, IJCNN2007Conference Proceedings,2007:242~247
[122] S. Wan, L. Zhang and Q.J. Zhang, Application of artificial neural networks forelectromagnetic modeling and computational electromagnetics, IEEE MidwestSymposium on Circuits and Systems (Knoxville, Tennessee) August2008
[123] Gebraeel Nagi Z (IEEE), Lawley Mark A., A neural network degradationmodel for computing and updating residual life distributions, IEEETransactions on Automation Science and Engineering, January2008,5(1):154~163
[124] H. Kabir, M. Yu, and Q.J. Zhang, State-of-the-art microwave filter modelingand design using neural network technique, Progress in ElectromagneticResearch Symposium (Hangzhou, China) March2008
[125]王光研，许宝杰，弹性BP神经网络的旋转机械故障诊断，北京：北京机械工业学院学报，2007，9~12
[126] Hong, Zhenya Ha Variable slap BP algorithm which prunes away redundantconnections dynamically Beijing: IJCNN,1992,2:441~445
[127] Zhang Y. D., Wu I. N., Weights Optimization of Neural Network via ImprovedBCO Approach[J], Progress in Electromagnetic Research,2008,83:185~198
[128] Constantinescu, Lazarescu, Tahboub, Geometrical form recognition using"one-step-secant" algorithm in case of neural network, University"Politehnica" of Bucharest, Scientific Bulletin Series C: Electrical Engineering,2008,70(2):15~28
[129] Rao H.I.K, Farhang-Boroujeny B., Analysis of the Stereophonic LMS/NewtonAlgorithm and Impact of Signal Nonlinearity on Its Convergence Behavior,IEEE Transactions on Signal Processing,2010,58(12):6080~6092
[130] Finsterle Stefan, Kowalsky Michael B., A truncated Levenberg-Marquardtalgorithm for the calibration of highly parameterized nonlinear models,Computers and Geosciences,2011,37(6):731~738
[131]田长国，有线电视双向HFC网络上行信道噪声分析，有线网络，2002，(3)：35~37
[132]王淦平，于凤芹，王灿忠，等，HFC反向通道噪声监测和抑制系统，电视技术，2007，31(5)：69~71
[133]陈韬， HFC反向通道噪声与干扰信号研究，电视技术，2008，(5)：52~54
[134]李慧，杨东梅，沈洁，等，BP神经网络在路径规划中的应用，应用科技，2004，31(9)：15~17
[135] YANG J. M., Sliding mode motion control of nonholonomic mobile, IEEEControl Systems,1999,(1):15~23
[136] Chun-Yi, Robust motion/forcecontrol of mechanical systerms with classicalnonholonomic constraints, IEEE Automation Control,1998,39(3):0~61
[137] YAMAMOTO M., USHIMI N., MOHRI A., Sensor-based navigation formobile robots using taget direction sensor, Jourual of the Rohoties Society ofJapan,1995,13(8):l130~l137
[138]冯居易，郭晔，基于LM算法的石油期货价格预测研究，技术经济与管理研究，2009，(5)：19~21
[139] Moller M. F. A, scaled conjugate gradient algorithm for eficient training ofartificial neural Networks,1992,(6):301~310
[140] Deepak Mishra, Abhishek Yadav, Sudipta Ray, and Prem K. Kalra,Levenberg-Marquardt Learning Algorithm for Integrate and FireNeuronModel[J], Neural Information Processing—Letters and Reviews,2005,9
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |