基于混沌和神经网络的时域参数测试研究及其在示波器中的应用
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摘要
本文从原理上阐述基于混沌理论的检测技术的可行性，根据混沌检测模型和神经网络检测模型在检测应用中存在的诸多需要研究的问题，研究混沌检测模型、神经网络检测模型、以及二者结合的复合检测模型的原理，提出应用混沌原理和神经网络原理相结合的检测技术检测混沌背景中的信号的时域参数。这是一个很有应用前景的研究方向。论文逆向运用混沌测量的原理，突破现有的理论，探索新的检测原理方法，直接在混沌状态下构建检测微弱信号时域参数的混沌模型和神经网络模型的方法，更有效地提取信号参数。同时，针对时域测试系统的本身动态特性属于非线性，寻找数学描述模型困难而难于校正的问题，运用逆系统理论，构建神经网络逆系统，获取原系统的非线性动态特性方法。论文重点研究建模算法、模型结构和建模方法，力求拓宽混沌和神经网络理论测试技术在时域测试、电路动态参数和瞬态参数捕获方面的应用范围，提高检测精度。
论文结合时域测试的典型仪器——数字示波器，将研究成果应用到数字示波器中。针对数字示波器捕获微弱触发信号能力差，不能测量微弱信号，采样经典的理论校准静态和动态参数等许多问题，论文着力研究增强其测量微弱信号和捕获微弱触发信号的能力；建立基于混沌理论校准模型；神经网络逆系统模型，创新静态、稳态和动态校准方法。
在课题研究中，作者主要做了以下几个方面的工作：
(1)对神经网络识别和检测信号进行分析和研究。研究了空间分割的竞争神经网络识别规则模拟信号类别的算法，提出了将竞争神经网络应用在数字示波器中识别规则信号类别，为选择内插算法提供依据。
(2)对Elman时空网络结构、稳定性及应用研究。讨论了Elman时空网络的结构和学习方法，重点研究了应用Elman时空网络测量时域信号的有效性和问题。研究了改进型Elman时空网络的算法和稳定性，并通过仿真证明了改进网络时域测量的优点。把混沌和神经网络结合起来，为构建了新的时域测量模型奠定了基础。
(3)研究混沌背景下的微弱周期信号的检测。用Duffing-Holmes方程构建混沌测量模型，检测微弱周期性信号的频率；利用二维Henon映射的混沌检测模型，检测微弱的触发信号，应用在数字示波器时基中。
(4)基于混沌和神经网络的微弱瞬时信号的检测的研究。研究基于FP算法的前向网络的结构和设计方法，构建了基于混沌背景下的微弱瞬时信号测量模型，在混沌状态下直接检测混沌背景下的瞬时信号。
(5)基于混沌和神经网络的微弱信号时域参数检测的研究。研究了混沌系统和神经网络检测模型和方法，在混沌状态下直接获取信号时域参数。同时，深入研究了时空神经网络的结构，获得基于混沌的神经网络的微弱时域信号检测模型的建模的依据。
(6)DSO校准和“NTN”校正方法中kick-out脉冲研究。研究数字示波器电压测量准确度、时基误差的估计和上升时间的测量和校准方法，建立了静态参数的混沌校准的模型。提出运用神经网络逆系统的方法，解决数字示波器动态参数校准的新思路。深入研究“NTN”校正方法和kick-out脉冲，为宽带高速数字示波器校准提供理论基础。
To solve the problems that have existed in the detection applications of chaos modeland neural network model,the feasibility of detecting technology based on chaos theoryis elaborated in terms of theory in this dissertation.The principles of chaos detectionmodel,neural network detection model and the composite detection model of theaforementioned are further studied.The time-domain parameters to detect the signals inthe chaotic background by adopting the detecting technology in the appliance of thechaos theory and neural network theory are presented.This study demonstrates positiveapplications by reversing the existing principles.By reversing the chaos detectionprinciple,a new method to extract signal parameters more effectively by building thechaos model and the neural network model to detect the weak-signal time-domainparameters in the chaotic state directly,has been explored.Simultaneously,because ofthe dynamic and non-linear characteristics of time-domain detection system,and thedifficulties in updating the mathematical model,a neural network inverse system hasbeen built to obtain the nonlinear dynamic characteristics of original system by usingthe inverse system theory.In essence,the modeling algorithm,the model's structure andmodeling methods are studied in depth in order to widen the application scope of usingchaos and neural network theory in the time-domain detection and the circuit dynamicparameters to improve the detection accuracy.
The research results are applied in digital oscilloscope,the typical time-domaindetection equipment.To solve the problems of incapability in detecting weak signalswith the digital oscilloscope for its poor performance in capturing weak trigger signalsand invalidation of updating the static and dynamic parameters with the classic theories,this dissertation are focused on enhancing the capability of DSO weak signal detectionand weak trigger signal capture.Calibration models based on chaos theory and inversesystem model based on neural network are constructed in this dissertation.Innovativestatic,steady-state and dynamic calibration methods are also recreated.
In this research,the author has contributed in the following major areas:
Ⅰ.Analysis and research on the neural network to identify and detect signals.Thealgorithms of space division competition neural networks to identify the regularanalog signal classifications are studied.The opinion which identifies the signaltype with the digital oscilloscope by competition neural network provides a basisfor the selection of interpolation algorithm.
Ⅱ.Research on structure,stability and applied of Elman space-time network.Thestructure and learning methods of Elman time-space network,especially thevalidity of its application to detect time-domain signals are discussed.Thealgorithm and stability of improved Elman space-time network are studied and itsadvantages are also proved by emulation.A new time-domain detection model hasbeen constructed on the basis of chaos theory and neural network.
Ⅲ.Research on the weak periodic signal detection in the chaotic background.Chaoticdetecting model is constructed by Duffing-Holmes equation to detect weakperiodic signal frequency;and the applications of two-dimension Henon map indetecting weak trigger signals and digital oscilloscope time-base are discussed.
Ⅳ.Research on weak transient signals detecting based on chaos and neural networktechnology.The structure of front-faced network and design method based on FPalgorithm are studied.A detection model to detect weak transient signals directlyin the chaotic background is constructed.
Ⅴ.Research on the weak signal time-domain parameters detection based on chaosand neural network technology.The detection model and method of chaoticsystem and neural network to obtain time-domain signal parameters directly in thechaotic state are researched.The structure of space-time neural network isfurther-studied,based on which the weak time-domain signal detection model byusing the chaotic neural network has been constructed.
Ⅵ.Research on the kick-out pulse in DSO calibration and“NTN”correction methods.The accuracy,time-based error estimation and the rise-time detection andcalibration methods in the application of digital oscilloscope voltage are studied aswell as chaos calibration model of static parameters is constructed.New ideas toadopt the neural network inverse system to solve the DSO dynamic parameterscalibration are proposed.The“NTN”updating methods and kick-out pulse are studied to provide theoretical basis for the broadband high-speed digitaloscilloscope calibration.
论文结合时域测试的典型仪器——数字示波器，将研究成果应用到数字示波器中。针对数字示波器捕获微弱触发信号能力差，不能测量微弱信号，采样经典的理论校准静态和动态参数等许多问题，论文着力研究增强其测量微弱信号和捕获微弱触发信号的能力；建立基于混沌理论校准模型；神经网络逆系统模型，创新静态、稳态和动态校准方法。
在课题研究中，作者主要做了以下几个方面的工作：
(1)对神经网络识别和检测信号进行分析和研究。研究了空间分割的竞争神经网络识别规则模拟信号类别的算法，提出了将竞争神经网络应用在数字示波器中识别规则信号类别，为选择内插算法提供依据。
(2)对Elman时空网络结构、稳定性及应用研究。讨论了Elman时空网络的结构和学习方法，重点研究了应用Elman时空网络测量时域信号的有效性和问题。研究了改进型Elman时空网络的算法和稳定性，并通过仿真证明了改进网络时域测量的优点。把混沌和神经网络结合起来，为构建了新的时域测量模型奠定了基础。
(3)研究混沌背景下的微弱周期信号的检测。用Duffing-Holmes方程构建混沌测量模型，检测微弱周期性信号的频率；利用二维Henon映射的混沌检测模型，检测微弱的触发信号，应用在数字示波器时基中。
(4)基于混沌和神经网络的微弱瞬时信号的检测的研究。研究基于FP算法的前向网络的结构和设计方法，构建了基于混沌背景下的微弱瞬时信号测量模型，在混沌状态下直接检测混沌背景下的瞬时信号。
(5)基于混沌和神经网络的微弱信号时域参数检测的研究。研究了混沌系统和神经网络检测模型和方法，在混沌状态下直接获取信号时域参数。同时，深入研究了时空神经网络的结构，获得基于混沌的神经网络的微弱时域信号检测模型的建模的依据。
(6)DSO校准和“NTN”校正方法中kick-out脉冲研究。研究数字示波器电压测量准确度、时基误差的估计和上升时间的测量和校准方法，建立了静态参数的混沌校准的模型。提出运用神经网络逆系统的方法，解决数字示波器动态参数校准的新思路。深入研究“NTN”校正方法和kick-out脉冲，为宽带高速数字示波器校准提供理论基础。
To solve the problems that have existed in the detection applications of chaos modeland neural network model,the feasibility of detecting technology based on chaos theoryis elaborated in terms of theory in this dissertation.The principles of chaos detectionmodel,neural network detection model and the composite detection model of theaforementioned are further studied.The time-domain parameters to detect the signals inthe chaotic background by adopting the detecting technology in the appliance of thechaos theory and neural network theory are presented.This study demonstrates positiveapplications by reversing the existing principles.By reversing the chaos detectionprinciple,a new method to extract signal parameters more effectively by building thechaos model and the neural network model to detect the weak-signal time-domainparameters in the chaotic state directly,has been explored.Simultaneously,because ofthe dynamic and non-linear characteristics of time-domain detection system,and thedifficulties in updating the mathematical model,a neural network inverse system hasbeen built to obtain the nonlinear dynamic characteristics of original system by usingthe inverse system theory.In essence,the modeling algorithm,the model's structure andmodeling methods are studied in depth in order to widen the application scope of usingchaos and neural network theory in the time-domain detection and the circuit dynamicparameters to improve the detection accuracy.
The research results are applied in digital oscilloscope,the typical time-domaindetection equipment.To solve the problems of incapability in detecting weak signalswith the digital oscilloscope for its poor performance in capturing weak trigger signalsand invalidation of updating the static and dynamic parameters with the classic theories,this dissertation are focused on enhancing the capability of DSO weak signal detectionand weak trigger signal capture.Calibration models based on chaos theory and inversesystem model based on neural network are constructed in this dissertation.Innovativestatic,steady-state and dynamic calibration methods are also recreated.
In this research,the author has contributed in the following major areas:
Ⅰ.Analysis and research on the neural network to identify and detect signals.Thealgorithms of space division competition neural networks to identify the regularanalog signal classifications are studied.The opinion which identifies the signaltype with the digital oscilloscope by competition neural network provides a basisfor the selection of interpolation algorithm.
Ⅱ.Research on structure,stability and applied of Elman space-time network.Thestructure and learning methods of Elman time-space network,especially thevalidity of its application to detect time-domain signals are discussed.Thealgorithm and stability of improved Elman space-time network are studied and itsadvantages are also proved by emulation.A new time-domain detection model hasbeen constructed on the basis of chaos theory and neural network.
Ⅲ.Research on the weak periodic signal detection in the chaotic background.Chaoticdetecting model is constructed by Duffing-Holmes equation to detect weakperiodic signal frequency;and the applications of two-dimension Henon map indetecting weak trigger signals and digital oscilloscope time-base are discussed.
Ⅳ.Research on weak transient signals detecting based on chaos and neural networktechnology.The structure of front-faced network and design method based on FPalgorithm are studied.A detection model to detect weak transient signals directlyin the chaotic background is constructed.
Ⅴ.Research on the weak signal time-domain parameters detection based on chaosand neural network technology.The detection model and method of chaoticsystem and neural network to obtain time-domain signal parameters directly in thechaotic state are researched.The structure of space-time neural network isfurther-studied,based on which the weak time-domain signal detection model byusing the chaotic neural network has been constructed.
Ⅵ.Research on the kick-out pulse in DSO calibration and“NTN”correction methods.The accuracy,time-based error estimation and the rise-time detection andcalibration methods in the application of digital oscilloscope voltage are studied aswell as chaos calibration model of static parameters is constructed.New ideas toadopt the neural network inverse system to solve the DSO dynamic parameterscalibration are proposed.The“NTN”updating methods and kick-out pulse are studied to provide theoretical basis for the broadband high-speed digitaloscilloscope calibration.
引文
[1]常城，孙尧．基于神经网络实现多种数字信号调制方式的自动识别．哈尔滨工程大学学报,2003，24(6)：651-655
[2]R.K.Hartana,G.G.Richards.Harmonic source monitoring and identification using neural networks.IEEE Transaction on Power Systems,1990,5(4):1098-1104
[3]张泾周，王艳芳，张光磊，等．多层前向神经网络在ECG信号滤波中的应用．陕西科技大学学报，2006，24(2)：81-85
[4]蒋传纪，郑正奇，李宗标．用分形和神经网络算法改善仪表精度和响应时间．仪器仪表学报，2004，25(1)：81-85
[5]Daniel Massicotte,Sylvie Legendre,Andrzej Barwicz.Neural-Network-Based Method of Calibration and Measurand Reconstruction for a High-Pressure Measuring System.IEEE Transactions On Instrumentation And Measurement,1998,47(2):362-370
[6]A.Bernieri,G.Betta,C.Liguori,et al.A neural network approach to instrument fault detection and isolation.IEEE Transactions On Instrumentation And Measurement,1995,44(3):747-750
[7]Haizhuang Kang,Qingping Yang,Clive Butler.Modeling and measurement accuracy enhancement of flue gas flow using neural networks.IEEE Transactions On Instrumentation and Measurement,1998,47(5):1379-1384
[8]Andrea Bemieri,Giovanni Betta,Consolatina Liguori,et al.Neural networks and pseudo-measurements for real-time monitoring of distribution system.IEEE Transactions on Instrumentation and Measurement,1996,45(2):645-650
[9]李月，杨宝俊，石要武．色噪声背景下微弱正弦信号的混沌检测．物理学报，2003，52(3)：526-530
[10]路鹏，李月．微弱正弦信号幅值混沌检测的一种改进方案．电子学报，2005，33(3)：527-529
[11]李瑜，章新华，肖毅，等．杜芬振子阵列实现弱正弦信号频率检测．系统仿真学报，2006，1 8(9)：2650-2656
[12]Guanyu Wang,Dajun Chen,Jianya Lin,et al.The application of chaotic oscillators to weak signal detection.IEEE Transactions on Industrial Electronics,April,1999,46(2):440-444
[13]G.Kolumban,B.Vizvari,A.Mogel.,et al.Chaotic systems:a challenge for measurement and analysis.IEEE Instrumentation and Measurement Technology Conforence,1996,1396-1401
[14]Donald L,Birx.,Stephen J.Pipenberg.Chaotic oscillators and complex mapping feed forward networks (CMFFNS) for signal detection in noisy environments.IEEE International Joint Conferents on Neural Networks.1992,Voi.2:881-887
[15]袁继敏，李小玲，蒋斌，等．基于混沌控制的嵌入式系统在示波器中的应用．华中科技大学学报，2005，33(12s)：346-347，359
[16]Warren S,Mcculloch,Walter Pitts.A logical calculus of the ideas immanent in nervous activity.Bulletin of Mathematical Biophysics,1943,5(2):115-133
[17]Bernard Widrow,Marcian E.Hoff.Adaptive Switching Circuits.1960 IREWESCON Convention Record,1960,4:96-104
[18]Grossberg S.Adaptive Pattern Classification and Universal Recording.Part I:Parallel Development and Coding of Neural Feature Detectors,Biological Cybernetics,1976,23:121-134
[19]T.Kohonen.Automatic formation of Topolgical Maps in self-orgnizing systems.Proceedings of the 2nd Scandinavian Conf on ImageAnalysis.,1981,25:214-220
[20]T.Kohonen.Self-organizing formation of Topologically Correct Feature Maps.Biological Cybernetics,1982,43:59-69
[21]J.J.Hopfiele.Neural Networks and Physical System with Emergent Collective Computational Abilities.Proc.of Natl.Acad.Sci.,1982,(79):2254-2558
[22]D.E.Rumenlhart,G.E.Hinton.,R.J.Williams.Learnig representations by back propagation errors.Nature,1986,323(9):533-536
[23]K.S.Narendra,K.Parthasarathy,et al.Identification and Control of Dynamical Systems Using Neural Networks.IEEE Trans.on Neural Networks,1990,1 (1):4-27
[24]C.L Giles.,M.W.Goudreau.Optical Architectures for Neural Computing and Neural NetworksRouting in optical multistage interconnection networks:a neural network solution..Lightwave Technology,1995,13(6):1111-1115
[25]李晖，顾学迈．通信网络中缩减的Hopfield神经网络路由算法．哈尔滨工业大学学报，2007，39(7)：1093-1098
[26]S.C.Stubberud,K.A.Kramer,J.A.Grerimia.Measurement augmentation to compensate for sensor registration using a neural Kalman fliter.Instrumentation and measurement technology conference proceeding.(IMTC2007),2007:1-6
[27]高小榕，杨福生．采用同伦BP算法进行多层前向神经网络的训练．计算机学报，1 996，19(9)：687-694
[28]曾文华．基于双隐层动态递归神经网络的航煤比重软测量．仪器仪表学报，2002，23(3)：261-264
[29]董长虹．Matlab神经网络与应用[M]．北京：国防工业出版社，2005，172-177
[30]廖晓昕．论Hopfield神经网络中物理参数的数学内蕴．中国科学(E辑)，2003，33(2)：127-136
[31]廖晓昕．细胞神经网络的数学理论(I)．中国科学(A辑)，1994，24(9)：902-910
[32]申金媛，常胜江，张延火斤，等．基于联想存储级联WTA模型的旋转不变识别．光学学报，1997，17(10)：1352-1356
[33]郝柏林．混沌与分形：郝柏林科普文集[M]．海科学技术出版社，2004，85-100
[34]苗东升，刘华杰．混沌学纵横论[M]．中国人民大学出版社，1994，80-90
[35]陈奉苏．混沌学及其应用[M]．中国电力出版社，1998，1-3
[36]王树禾．微分方程模型与混沌[M]．中国科学技术大学出版社，1999，431-441
[37]庄镇泉，王熙法，王东生．神经网络与神经计算机[M]．科学出版社，1992，1-4
[38]T.Li,A J.Yorke,AmetMath.Monthly,1975,82,985
[39]F.Takens.Detecting strange attractors in fluid turbulence,in:Dynamical system and Turbulence.Eds.D.Rand and L.S.Young(Springer),1981,:366-381
[40]李春福．关于混沌信号的产生、处理及其在通信系统中应用的若干研究[D电子科技大学博士论文]．成都：电子科技大学，2001，87-89
[41]Grassberger Peter,I.Procaccia.Dimensions and entropies of strange attractors from a fluctuating dynamics approach.Physica D,1984,13(1-2):34-54
[42]Y.V.Andreyev,Y.L.Belsky,Alexander S.Dmitfiev.Information processing using dynamical chaos:neural networks implementation.IEEE Trans.on Neural Networks,1996,7(2):290-299
[43]Daw-Tung Lin,Judith E Dayhoff.,Panos A Ligomenides.Adaptive time-delay neural network for temporal correlation and prediction.Intelligent Robots and Computer Vision XI:Biological,Neural Net,and 3D Methods,1992,Vol.1826:170-181
[44]Tsui Fu-Chiang,Sun Mingui,Li Ching-Chung,et al,Recurrent neural networks and discrete wavelet transform for time series modeling and prediction.Acoustics,Speech,and Signal Processing,1995.ICASSP-95.,1995.Vol 5,:3359-3362
[45]S.Haykin,Xiao Bo,Li.Detection of signals in chaos.Proc.IEEE,1995,83(1):95-122
[46]T.S.Satish,Bukkapatnama,R.T.Soundar.The neighborhood method and its coupling with the wavelet met hod for signal separation of chaotic signals.Signal Processing,2002,82:1351-1374
[47]殷培强，俞立，南余荣，等．基于分段李雅普诺夫函数的永磁同步电机混沌系统非脆弱模糊控制．中国电机工程学报，2006，26(24)：143-147
[48]J.H.Chen,K.T.chau,C.C.chau,et al.Subharmonics and chaos in switched reluctance motor drives.IEEE Trans.Energy Conversion,2002,17(1):73-78
[49]Y.Gao,K.T chau.Hopfbifurcation and chaos in synchronous reluctance motor drives.IEEE Trans.Energy Conversion,2004,19(2):296-302
[50]Li Zhong,Park,Jin Bae Park,Young Hoon Joo,et al.Bifurcations and chaos in a permanent-magnet synchronous motor.IEEE Trans.Circuits and systems,2002,49(3):383-387
[51]J.H Chen.,K.T Chau.,S.M.Siu,et al.Experimental stabilization of chaos in a voltage-mode DC drive system.IEEE Trans.Circuits and systems,2000,47(7):1093-1095
[52]E.A.Rogers,R.Kalra,R.D.Schroll,et al.Generalized synchronization of spatiotemporal chaos in a liquid crystal spatial light moduatot.Phys.Rev.Lett.,2004,93(8):84101-84104
[53]Atsushi Uchida,Ryan McAllister,Riccardo Meucci Generalized.synchronization of chaos in identical systems with hidden degrees of freedom.Phys.Rev.Lett.,2003,91(17):174101-174104
[54]A.Venkatesana.,S.Parthasarathya.,M.Lakshmanan.Occurrence of multiple period doubling bifurcation route to chaos in periodically pulsed chaotic dynamical systems.Chaos.Solitons and Fractals,2003,18:891-898
[55]Guanyu Wang,Sailing He.A quantitative study on detection and estimaiton of weak signals by using chaotic duffing oscillators.IEEE Transactions on Circuits and Systems-I:Fundamental Theory and Application,2003,50(7):945-953
[56]张洪钧，王文才．光学混沌[M]．上海科技教育出版社．1997，107-110
[57]方兆本．走出混沌[M]．湖南教育出版社，1995，1 13-1 16
[58]谢红梅．基于混沌理论的信号处理方法研究[D西北工业大学博士论文]，西北工业大学，2003，16-22
[59]龙运佳．混沌振动研究方法与实践[M]．清华大学出版社，1997，18-20
[60]陈士华，陆均安．混沌动力学初步[M]．武汉水利电力大学出版社，1998，38-62
[61]郑为敏．正反馈[M]．清华大学出版社，1998，18-22
[62]孙霞，吴自勤，黄畇．分形原理及应用[M]．中国科技大学出版社，2003，23-30
[63]陈琢，童勤业．混沌电路的温度特性及其在温度测量中的应用．电路与系统学报，2003，8(4)：21-24
[64]陈琢，钱鸣奇，童勤业．混沌态电阻测量电路的研究．电子测量与仪器学报，2003，17(3)：46-51
[65]童勤业，金敏，虞捷．“混沌”运算器的实现．电路与系统学报，2000，5(4)：33-37
[66]Y.C.Hsiao,P.C.Tung.Controlling chaos for nonautonomous systems by detecting unstable periodic orbits.Solitons and Fractals,2002,13:1043-1051
[67]A.R.Jose,.E.P.Gilberto,P.Chaos.control using small amplitude damping signals.Physics Letters A,2003,:196-205
[68]数字通信测量仪器编写组．数字通信测量仪器[M]．北京：人民邮电出版社，2007，39-41
[69]苏抗，王成华．数字存储示波器数据处理系统设计．南京航空航天大学学报，2006，38(6)：769-774
[70]Matteo Bertocco,Luca Garbin,Claudio.Correction of systematic effects in digitizing oscilloscope.2003,52(3):871-877
[71]H．P．Fleischheuer．示波器所面临的最新挑战．今日电子，2006，3：48-49
[72]关新平，范正平，陈彩莲，等．混沌控制及其在保密通信中的应用[M]．北京：国防工业出版社，2002，233-241
[73]R.F.Luo,H.H.Shao,Z.J.Zhang.Fuzzy-neural-net-based inferential control for a highpurity.Control Engineering Pratice,1995,3(1):31-40
[74]周其节，徐建闽．申经网络的控制系统的研究与展望．控制理论与应用，1992，9(6)：569-577
[75]陈恬，孙健国．基于相关性分析和神经网络的直接推力控制．南京航空航天大学学报，2005，37(2)：183-187
[76]张伏生，耿中行，葛耀中．电力系统谐波分析的高精度FFT算法．中国电机工程学报，1999，19(3)：63-66
[77]刘瑞兰，苏宏业，褚健．基于改进模糊神经网络的软测量建模方法．信息与控制，2003，32(4)：367-370
[78]杜殿林，左信，罗雄麟，等．人工神经网络软测量仪表延迟时间处理及动态特性研究．化工自动化及仪表，2005，32：47-49
[79]D'.Elia MARIA Grazia,Liguori Consolatina,Paciello Vincenzo,et al.Software customization to provide digital oscilloscope with enhanced period measurement features.IEEE Trans.Instrum.Meas,2006,55(2):493-500
[80]J.Schoukens,Y.Rolain,G.Simon.Fully automated spectral analysis of periodic signals.IEEE Transaction on Instrumentation and Measurement,2003,52(4):1021-1024
[81]N.Geckinli,D.Yavuz.Algorithm for pitch extraction using zero-crossing interval sequence.IEEE Trans.Acoust.Speech,Signal Process,1977,25(6):559-564
[82]J.T.Xi.,J.F.Chicaro.A new algorithm for improving the accuracy of periodic signal analysis.IEEE Transactions On Instrumentation And Measurement,1996,45(4):827-831
[83]M.W.Mak,K.W.Ku.,Y.L.Lu..On the improvement of the real time recurrent learning algorithm for recurrent neural networks.Neurocomputing,1992,(24):13-36
[84]F.M.Zeng,Y.T Chen.,J.M.Wu.Dynamic modeling and simulation of marine diesel engine using Elman networks.IEEE Int Conf neural networks and signal processing,2003,Vol.1:100-103
[85]Y.C Cheng.,W.M Qi.,W.Y Cai.Dynamic properties of elman and modified elman neural network.Proceedings of the First International Conference on Machine and Cybemetices,2002,Vol.2:637-640
[86]X.Y.Yang,D.P.Xu,X.J.Han,et al.Predictive functional control with modified elman neural network for reheated steam temperature.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics.,2005,Vol.8:4699-4703
[87]袁继敏，李小玲，古天祥．基于时空神经网络增强数字示波器功能的研究．电子科技大学学报，2007，36(5)：938-941
[88]A.Meyer-Base,V.Thummler.Local and Global Stability Analysis of an Unsupervised Competitive Neural Network.2008,19(2):346-351
[89]加卢什金．神经网络理论[M]．北京：清华大学出版社，2002，222-224
[90]E.Chiaruntoni,G.Acciani,F Vucca.Local Competitive Signals for an Unsupervised Competitive Neural Network.ISCAS 2000-IEEE International Symposium on Circuits and Systems,Vol.3:590-593
[91]Godoy Simoes,M.Massatoshi Furukawa,C.Mafra A.T.,et al.A novel Competitive Learning Neural Network Based Acoustic Transmission System for Oil-Well Monitoring.IEEE Transaction On Industry Applications,2000,36(2):484-491
[92]H.t.Lu S,Amari.Global Exponential Stability of Multitime Scale Competitive Neural Networks With Nonsmooth Functions.IEEE Transaction on Neural networks,2006,17(5):1152-1164
[93]Andrzej Materka,Shizuo Mizushina.Parametric Signal Restoration Using Artificial Neural Networks.IEEE Transaction on Biomedical Engineering,April,1996,43(4):357-372
[94]张军英，许进．二进前向人工神经网络理论[M]．西安电子科技大学出版社，2001，66-70
[95]向东阳，王公宝，马伟明，等．基于FFT和神经网络的非整数次谐波检测方法．中国电机工程学报，2005，25(9)：35-39
[96]袁继敏，古天祥，徐晨曦．基于空间分割前向竞争网络的信号识别．电子测量与仪器学报，2007，21(3)：15-19
[97]高晋占．微弱信号检测[M]．清华大学出版社，2004，1-3
[98]童勤业，严筱刚，孔军等．“混沌”理论在测量中的应用．电子与信息学报，1999，21(1)：42-44
[99]J.Theiler,S.Eubank.Test for nonlinearity in time series:the method of surrogate data.Physica D,1992,58:77-94
[100]D.T.kaplan,L.Glass.Direct test for determinism in time series.Phys Rev Lett,1992,64(4):427-430
[101]Alexei Potapov,J(u|¨)rgen Kurths.Correlation integral as a tool for distinguishing between Dynamics and statistics in time series data.Physica D,1998,120(3-4):369-385
[102]卫三民，秦荃华，孟晓风．离心机高精度转速控制系统．清华大学学报(自然科学版)，2001，41(3)：51-54
[103]李月，杨宝俊，石要武．B色噪声背景下微弱正弦信号的混沌检测．物理学报，March,2003，52(3)：526-530
[104]Wang Guanyu,Zheng Wei,He Sailing.Estimation of amplitude and phase of a weak signal by using the property of sensitive dependence of initial conditions of a nonlinear oscillator.Signal Processing,2002,82(1):103-105
[105]陈国华，盛昭瀚．基于Lypaunov指数的混沌时间序列识别．系统工厂理论方法应用，2003，12(4)：3 17-320
[106]温权，张勇传，程时杰．辨识混沌时间序列中的确定性．水电能源科学，2001，19(3)：72-75
[107]Y.C.Lai,D.Lerner.Eeffctive scealing regime for computing the correlation dimension from chaotic time series.Physica D:Nonlinear Phonomena,1995,115(1-2):1-18
[108]Y.C.Lai,D.Lerner,R.Hyaden.An upper bound for the proper delay time in chaotic time series analysis.Physics lettA,1996,218(1-2):30-34
[109]I.Matsuba,H.Suyari,S.Weon.Practical chaos time series analysis with financial applications.Proceeding of ICSP.2000,2000,Vol.1:227-265
[110]朱义东，李玉林．基于最大Lypaunov指数方法预测油田产量．西南石油学院学报，2005，27(3)：32-33
[111]田玉楚．混沌时间序列的时滞判定．物理学报，1997，46(3)：442-447
[112]肖方红，阎桂荣，韩宇航．混沌时序相空间重构参数确定的信息论方法．物理学报，2005，54(2)：550-556
[113]Y.C.Tian,F.Gao.Extraction of delay information from chaotic time series based on information entropy.Physica D,1997,108(1-2):113-118
[114]马红光，李夕海．相空间重构中嵌入维和时间延迟的选择．西安交通大学学报，2004，38(4)：335-338
[115]王海燕，盛昭瀚．混沌时间序列相空间重构参数的选取方法．东南大学学报(自然科学版)，2000，30(5)：113-117
[116]D.S.Broomhead,Gregory P.King.Extracting qualitative dynamics from experimental data.Physica D,1986,9:198-208
[117]袁坚，肖先赐．非线性时间序列的高阶奇异谱分析．物理学报，1998，47(6)：897-905
[118]孟庆芳，张强，潘金凤．四阶累积量用于最小嵌入维数估计的新方法．系统工程理论与实践，2005，9：83-88
[119]S.Heidari,C.L.Nikias.Characterizing chaotic attractors using fourth-order off-diagonalcumulant slices.Conference on signal Record of the 27th Asilomar conference Signal systems and computers.Pacific Grove,USA,1993,Vol.1:466-470
[120]L.Y.Cao.Practical method for determining the minimum embedding dimension of a scalar time series.Physica D,1997,110(1-2):43-50
[121]胡岗，萧井华，郑志刚．混沌控制[M]．上海科技教育出版社，2000，177-179
[122]李月，杨宝俊．混沌振子检测引论[M]．电子工业出版社，2004，30-35
[123]洪时中．非线性时间序列分析的最新进展及其在地球科学中的应用前景．地球科学进展，1999，14(6)：559-565
[124]聂春燕，石要武，衣文索．强噪声下利用混沌系统测量频率的新方法．传感器技术，2004，23(3)：57-59
[125]李月，杨宝俊，邓小英，等．谐波信号频率的混沌检测方法．电子与信息学报，2005，27(5)：73 1-733
[126]李月，石要武，王立群，等．纳伏级方波信号的混沌测量方法．计量学报，2003，4(10)：317-320
[127]李月，杨宝俊，石要武．用混沌振子检测淹没在强背景噪声中的方波信号．吉林大学自然科学学报，2001，4(2)：65-68
[128]李月，石要武，马海涛，等．湮没在色噪声背景下微弱方波信号的混沌检测方法．电子学报，2004，32(1)：87-90
[129]梁志国．JJF1057-1998数字存储示波器校准规范[M]．中国计量出版社，1999，8-15
[130]周开利，康耀红．神经网络模型及其MATLAB仿真程序设计[M]．清华大学出版社，2005，69-75
[131]魏海坤．申经网络结构设计的理论与方法[M]．国防工业出版社，2005，111-115
[132]陈曦，李庄伟．关于数字示波器电压测量准确度校准方法研究．上海计量测试，2006，(5)：30-33
[133]沈怀洋．数字示波器的校准．计量技术，2005，(5)：38-40
[134]梁志国，朱济杰．用周期倍差法评价数据采集系统的动特性．中国计量测试学会，1998，1095-1901
[135]W.L.Gans.Dynamic calibration of waveform recorders and oscilloscopes using pulse standards.IEEE Transaction on Instrumentation and Measurement,1990,39(6):952-957
[136]郭献宏．数字示波器垂直精度性能分析．2004，(8)：8-10
[137]梁志国．数字存储示波器计量标准建立中的几个问题．宇航计测技术，1999，19(4)：41-46
[138]林茂六，张喆．高速采样示波器中的时基失真及其估计．计量学报，2004，25(3)：266-269
[139]杜亮．数字示波器计量校准与应用中须注意的若干问题．中国测试技术，2005，31(6)：72-74
[140]庄双勇，朱霞辉．数字存储示波器水平时间扫描因数检定．电子测量技术，2002，(3)：32-33
[141]田晓华，余学锋，赵婉丽．数字存储示波器扫描时间因数的检定．计量与测试技术，2004，(9)：12-13
[142]G.N.Stenbakken,J.P.Deyst.Comparison of time base nonlinearity measurement techniques.IEEE Transaction on Instrumentation and Measurement,1998,47(1):34-39
[143]张莉莉，刘明亮，朱江淼．基于信号重构和系统辨识的上升时间测量．电子测量技术，2006,29(6)：57-59
[144]G.N.Stenbakken,J.P.Deyst.Time-base nonlinearity determination using iterated sine-fit analysis.IEEE Transaction on Instrumentation and Measurement,1998,47(5):1056-1061
[145]赵科佳，刘明亮，郁月华，等．宽带取样示波器上升时间与带宽的转换系数的研究．计量学报，2006，27(2)：160-163
[146]J.Versecht.Broadband sampling oscilloscope characterization with the nose to nose calibration procedure:a theoretical and practical analysis.IEEE Transaction on Instrumentation and Measurement,1995,44(6):991-997
[147]林茂六．高速采样信号数字内插理论与正弦内插算法研究．电子学报，2000，28(12)：8-10
[148]郭伟民，邓晓莉，李莉．关于数字示波器上升时间的探讨．宇航计测技术，2003，23(5)：31-39
[149]余学锋，于洁．示波器上升时间测量的误差分析．宇航计测技术，2002，22(1)：36-41
[150]C.M.Wang,P.D.Hale,K.J.Coakley Least-squares estimation of time-base distortion of sampling oscilloscopes.IEEE Transaction on Instrumentation and Measurement,1999,48(6):1324-1332
[151]马红梅，邓明纫，苏水金，等．70GHz取样示波器上升时间与频响的校准．宇航计测技术，2007，27(5)：6-9
[152]D.Henderson,A.G.Roddie.Calibration of fast sampling oscilloscopes.Meas Sci Technol,1990,(1):673-679
[153]A.Dienstfiey,P.D.Hale,D.A.Keenan,et al.Minimum-phase calibration of sampling oscilloscopes.IEEE Transaction on Microwave Theory And Techniques,2006,54(8):3197-3208
[154]T.S.Clement,P.D.Hale,D.F.Williams,et al.Calibration of sampling oscilloscopes with high-speed photodiodes.IEEE Transaction on Microwave Theory and Techniques,2006,54(8):3173-3181
[155]J.Verspencht.Broadband sampling oscilloscope characterization with the‘nose-to-nose’calibration procedure:a theoretical and practical analysis.IEEE Transaction on Instrumentation and Measurement,1995,44(6):991-997
[156]赵华，林茂六．基于Nose—to—Nose校准法的取样示波器冲激响应的一种算法．电子学报，2003,3 1(3)：365-367
[157]朱江淼，刘明亮，高昀．取样示波器nose-to-nose校准技术中kick-out脉冲的研究．中国计量学院学报，2003，14(3)：185-188
[158]D.R.Larson,N.G.Paulter.The effects of offset voltage on the amplitude and bandwidth of kick-out pulses used in the nose-to-nose sampler impulse response characterization method.IEEE Transaction on Instrumentation and Measurement,2001,50(4):872-876
[159]N.G.Paulter,D.R.Larson.An examination of the spectra of the“kick-out”pulses for a proposed sampling oscilloscope calibration method.IEEE Trans Instrum Meas,2001,50(5):1221-1223
[160]J.Verspecht,K.Rush.Individual characterization of broadband sampling oscilloscopes with a nose-to-nose calibration procedure.IEEE Trans Instrum Meas,1994,43(2):347-354
[161]袁继敏，李小玲，古天祥．宽带取样示波器“NTN”校正和Kick-Out脉冲研究．电子科技大学学报，2007，36(4)：737-739，747
[2]R.K.Hartana,G.G.Richards.Harmonic source monitoring and identification using neural networks.IEEE Transaction on Power Systems,1990,5(4):1098-1104
[3]张泾周，王艳芳，张光磊，等．多层前向神经网络在ECG信号滤波中的应用．陕西科技大学学报，2006，24(2)：81-85
[4]蒋传纪，郑正奇，李宗标．用分形和神经网络算法改善仪表精度和响应时间．仪器仪表学报，2004，25(1)：81-85
[5]Daniel Massicotte,Sylvie Legendre,Andrzej Barwicz.Neural-Network-Based Method of Calibration and Measurand Reconstruction for a High-Pressure Measuring System.IEEE Transactions On Instrumentation And Measurement,1998,47(2):362-370
[6]A.Bernieri,G.Betta,C.Liguori,et al.A neural network approach to instrument fault detection and isolation.IEEE Transactions On Instrumentation And Measurement,1995,44(3):747-750
[7]Haizhuang Kang,Qingping Yang,Clive Butler.Modeling and measurement accuracy enhancement of flue gas flow using neural networks.IEEE Transactions On Instrumentation and Measurement,1998,47(5):1379-1384
[8]Andrea Bemieri,Giovanni Betta,Consolatina Liguori,et al.Neural networks and pseudo-measurements for real-time monitoring of distribution system.IEEE Transactions on Instrumentation and Measurement,1996,45(2):645-650
[9]李月，杨宝俊，石要武．色噪声背景下微弱正弦信号的混沌检测．物理学报，2003，52(3)：526-530
[10]路鹏，李月．微弱正弦信号幅值混沌检测的一种改进方案．电子学报，2005，33(3)：527-529
[11]李瑜，章新华，肖毅，等．杜芬振子阵列实现弱正弦信号频率检测．系统仿真学报，2006，1 8(9)：2650-2656
[12]Guanyu Wang,Dajun Chen,Jianya Lin,et al.The application of chaotic oscillators to weak signal detection.IEEE Transactions on Industrial Electronics,April,1999,46(2):440-444
[13]G.Kolumban,B.Vizvari,A.Mogel.,et al.Chaotic systems:a challenge for measurement and analysis.IEEE Instrumentation and Measurement Technology Conforence,1996,1396-1401
[14]Donald L,Birx.,Stephen J.Pipenberg.Chaotic oscillators and complex mapping feed forward networks (CMFFNS) for signal detection in noisy environments.IEEE International Joint Conferents on Neural Networks.1992,Voi.2:881-887
[15]袁继敏，李小玲，蒋斌，等．基于混沌控制的嵌入式系统在示波器中的应用．华中科技大学学报，2005，33(12s)：346-347，359
[16]Warren S,Mcculloch,Walter Pitts.A logical calculus of the ideas immanent in nervous activity.Bulletin of Mathematical Biophysics,1943,5(2):115-133
[17]Bernard Widrow,Marcian E.Hoff.Adaptive Switching Circuits.1960 IREWESCON Convention Record,1960,4:96-104
[18]Grossberg S.Adaptive Pattern Classification and Universal Recording.Part I:Parallel Development and Coding of Neural Feature Detectors,Biological Cybernetics,1976,23:121-134
[19]T.Kohonen.Automatic formation of Topolgical Maps in self-orgnizing systems.Proceedings of the 2nd Scandinavian Conf on ImageAnalysis.,1981,25:214-220
[20]T.Kohonen.Self-organizing formation of Topologically Correct Feature Maps.Biological Cybernetics,1982,43:59-69
[21]J.J.Hopfiele.Neural Networks and Physical System with Emergent Collective Computational Abilities.Proc.of Natl.Acad.Sci.,1982,(79):2254-2558
[22]D.E.Rumenlhart,G.E.Hinton.,R.J.Williams.Learnig representations by back propagation errors.Nature,1986,323(9):533-536
[23]K.S.Narendra,K.Parthasarathy,et al.Identification and Control of Dynamical Systems Using Neural Networks.IEEE Trans.on Neural Networks,1990,1 (1):4-27
[24]C.L Giles.,M.W.Goudreau.Optical Architectures for Neural Computing and Neural NetworksRouting in optical multistage interconnection networks:a neural network solution..Lightwave Technology,1995,13(6):1111-1115
[25]李晖，顾学迈．通信网络中缩减的Hopfield神经网络路由算法．哈尔滨工业大学学报，2007，39(7)：1093-1098
[26]S.C.Stubberud,K.A.Kramer,J.A.Grerimia.Measurement augmentation to compensate for sensor registration using a neural Kalman fliter.Instrumentation and measurement technology conference proceeding.(IMTC2007),2007:1-6
[27]高小榕，杨福生．采用同伦BP算法进行多层前向神经网络的训练．计算机学报，1 996，19(9)：687-694
[28]曾文华．基于双隐层动态递归神经网络的航煤比重软测量．仪器仪表学报，2002，23(3)：261-264
[29]董长虹．Matlab神经网络与应用[M]．北京：国防工业出版社，2005，172-177
[30]廖晓昕．论Hopfield神经网络中物理参数的数学内蕴．中国科学(E辑)，2003，33(2)：127-136
[31]廖晓昕．细胞神经网络的数学理论(I)．中国科学(A辑)，1994，24(9)：902-910
[32]申金媛，常胜江，张延火斤，等．基于联想存储级联WTA模型的旋转不变识别．光学学报，1997，17(10)：1352-1356
[33]郝柏林．混沌与分形：郝柏林科普文集[M]．海科学技术出版社，2004，85-100
[34]苗东升，刘华杰．混沌学纵横论[M]．中国人民大学出版社，1994，80-90
[35]陈奉苏．混沌学及其应用[M]．中国电力出版社，1998，1-3
[36]王树禾．微分方程模型与混沌[M]．中国科学技术大学出版社，1999，431-441
[37]庄镇泉，王熙法，王东生．神经网络与神经计算机[M]．科学出版社，1992，1-4
[38]T.Li,A J.Yorke,AmetMath.Monthly,1975,82,985
[39]F.Takens.Detecting strange attractors in fluid turbulence,in:Dynamical system and Turbulence.Eds.D.Rand and L.S.Young(Springer),1981,:366-381
[40]李春福．关于混沌信号的产生、处理及其在通信系统中应用的若干研究[D电子科技大学博士论文]．成都：电子科技大学，2001，87-89
[41]Grassberger Peter,I.Procaccia.Dimensions and entropies of strange attractors from a fluctuating dynamics approach.Physica D,1984,13(1-2):34-54
[42]Y.V.Andreyev,Y.L.Belsky,Alexander S.Dmitfiev.Information processing using dynamical chaos:neural networks implementation.IEEE Trans.on Neural Networks,1996,7(2):290-299
[43]Daw-Tung Lin,Judith E Dayhoff.,Panos A Ligomenides.Adaptive time-delay neural network for temporal correlation and prediction.Intelligent Robots and Computer Vision XI:Biological,Neural Net,and 3D Methods,1992,Vol.1826:170-181
[44]Tsui Fu-Chiang,Sun Mingui,Li Ching-Chung,et al,Recurrent neural networks and discrete wavelet transform for time series modeling and prediction.Acoustics,Speech,and Signal Processing,1995.ICASSP-95.,1995.Vol 5,:3359-3362
[45]S.Haykin,Xiao Bo,Li.Detection of signals in chaos.Proc.IEEE,1995,83(1):95-122
[46]T.S.Satish,Bukkapatnama,R.T.Soundar.The neighborhood method and its coupling with the wavelet met hod for signal separation of chaotic signals.Signal Processing,2002,82:1351-1374
[47]殷培强，俞立，南余荣，等．基于分段李雅普诺夫函数的永磁同步电机混沌系统非脆弱模糊控制．中国电机工程学报，2006，26(24)：143-147
[48]J.H.Chen,K.T.chau,C.C.chau,et al.Subharmonics and chaos in switched reluctance motor drives.IEEE Trans.Energy Conversion,2002,17(1):73-78
[49]Y.Gao,K.T chau.Hopfbifurcation and chaos in synchronous reluctance motor drives.IEEE Trans.Energy Conversion,2004,19(2):296-302
[50]Li Zhong,Park,Jin Bae Park,Young Hoon Joo,et al.Bifurcations and chaos in a permanent-magnet synchronous motor.IEEE Trans.Circuits and systems,2002,49(3):383-387
[51]J.H Chen.,K.T Chau.,S.M.Siu,et al.Experimental stabilization of chaos in a voltage-mode DC drive system.IEEE Trans.Circuits and systems,2000,47(7):1093-1095
[52]E.A.Rogers,R.Kalra,R.D.Schroll,et al.Generalized synchronization of spatiotemporal chaos in a liquid crystal spatial light moduatot.Phys.Rev.Lett.,2004,93(8):84101-84104
[53]Atsushi Uchida,Ryan McAllister,Riccardo Meucci Generalized.synchronization of chaos in identical systems with hidden degrees of freedom.Phys.Rev.Lett.,2003,91(17):174101-174104
[54]A.Venkatesana.,S.Parthasarathya.,M.Lakshmanan.Occurrence of multiple period doubling bifurcation route to chaos in periodically pulsed chaotic dynamical systems.Chaos.Solitons and Fractals,2003,18:891-898
[55]Guanyu Wang,Sailing He.A quantitative study on detection and estimaiton of weak signals by using chaotic duffing oscillators.IEEE Transactions on Circuits and Systems-I:Fundamental Theory and Application,2003,50(7):945-953
[56]张洪钧，王文才．光学混沌[M]．上海科技教育出版社．1997，107-110
[57]方兆本．走出混沌[M]．湖南教育出版社，1995，1 13-1 16
[58]谢红梅．基于混沌理论的信号处理方法研究[D西北工业大学博士论文]，西北工业大学，2003，16-22
[59]龙运佳．混沌振动研究方法与实践[M]．清华大学出版社，1997，18-20
[60]陈士华，陆均安．混沌动力学初步[M]．武汉水利电力大学出版社，1998，38-62
[61]郑为敏．正反馈[M]．清华大学出版社，1998，18-22
[62]孙霞，吴自勤，黄畇．分形原理及应用[M]．中国科技大学出版社，2003，23-30
[63]陈琢，童勤业．混沌电路的温度特性及其在温度测量中的应用．电路与系统学报，2003，8(4)：21-24
[64]陈琢，钱鸣奇，童勤业．混沌态电阻测量电路的研究．电子测量与仪器学报，2003，17(3)：46-51
[65]童勤业，金敏，虞捷．“混沌”运算器的实现．电路与系统学报，2000，5(4)：33-37
[66]Y.C.Hsiao,P.C.Tung.Controlling chaos for nonautonomous systems by detecting unstable periodic orbits.Solitons and Fractals,2002,13:1043-1051
[67]A.R.Jose,.E.P.Gilberto,P.Chaos.control using small amplitude damping signals.Physics Letters A,2003,:196-205
[68]数字通信测量仪器编写组．数字通信测量仪器[M]．北京：人民邮电出版社，2007，39-41
[69]苏抗，王成华．数字存储示波器数据处理系统设计．南京航空航天大学学报，2006，38(6)：769-774
[70]Matteo Bertocco,Luca Garbin,Claudio.Correction of systematic effects in digitizing oscilloscope.2003,52(3):871-877
[71]H．P．Fleischheuer．示波器所面临的最新挑战．今日电子，2006，3：48-49
[72]关新平，范正平，陈彩莲，等．混沌控制及其在保密通信中的应用[M]．北京：国防工业出版社，2002，233-241
[73]R.F.Luo,H.H.Shao,Z.J.Zhang.Fuzzy-neural-net-based inferential control for a highpurity.Control Engineering Pratice,1995,3(1):31-40
[74]周其节，徐建闽．申经网络的控制系统的研究与展望．控制理论与应用，1992，9(6)：569-577
[75]陈恬，孙健国．基于相关性分析和神经网络的直接推力控制．南京航空航天大学学报，2005，37(2)：183-187
[76]张伏生，耿中行，葛耀中．电力系统谐波分析的高精度FFT算法．中国电机工程学报，1999，19(3)：63-66
[77]刘瑞兰，苏宏业，褚健．基于改进模糊神经网络的软测量建模方法．信息与控制，2003，32(4)：367-370
[78]杜殿林，左信，罗雄麟，等．人工神经网络软测量仪表延迟时间处理及动态特性研究．化工自动化及仪表，2005，32：47-49
[79]D'.Elia MARIA Grazia,Liguori Consolatina,Paciello Vincenzo,et al.Software customization to provide digital oscilloscope with enhanced period measurement features.IEEE Trans.Instrum.Meas,2006,55(2):493-500
[80]J.Schoukens,Y.Rolain,G.Simon.Fully automated spectral analysis of periodic signals.IEEE Transaction on Instrumentation and Measurement,2003,52(4):1021-1024
[81]N.Geckinli,D.Yavuz.Algorithm for pitch extraction using zero-crossing interval sequence.IEEE Trans.Acoust.Speech,Signal Process,1977,25(6):559-564
[82]J.T.Xi.,J.F.Chicaro.A new algorithm for improving the accuracy of periodic signal analysis.IEEE Transactions On Instrumentation And Measurement,1996,45(4):827-831
[83]M.W.Mak,K.W.Ku.,Y.L.Lu..On the improvement of the real time recurrent learning algorithm for recurrent neural networks.Neurocomputing,1992,(24):13-36
[84]F.M.Zeng,Y.T Chen.,J.M.Wu.Dynamic modeling and simulation of marine diesel engine using Elman networks.IEEE Int Conf neural networks and signal processing,2003,Vol.1:100-103
[85]Y.C Cheng.,W.M Qi.,W.Y Cai.Dynamic properties of elman and modified elman neural network.Proceedings of the First International Conference on Machine and Cybemetices,2002,Vol.2:637-640
[86]X.Y.Yang,D.P.Xu,X.J.Han,et al.Predictive functional control with modified elman neural network for reheated steam temperature.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics.,2005,Vol.8:4699-4703
[87]袁继敏，李小玲，古天祥．基于时空神经网络增强数字示波器功能的研究．电子科技大学学报，2007，36(5)：938-941
[88]A.Meyer-Base,V.Thummler.Local and Global Stability Analysis of an Unsupervised Competitive Neural Network.2008,19(2):346-351
[89]加卢什金．神经网络理论[M]．北京：清华大学出版社，2002，222-224
[90]E.Chiaruntoni,G.Acciani,F Vucca.Local Competitive Signals for an Unsupervised Competitive Neural Network.ISCAS 2000-IEEE International Symposium on Circuits and Systems,Vol.3:590-593
[91]Godoy Simoes,M.Massatoshi Furukawa,C.Mafra A.T.,et al.A novel Competitive Learning Neural Network Based Acoustic Transmission System for Oil-Well Monitoring.IEEE Transaction On Industry Applications,2000,36(2):484-491
[92]H.t.Lu S,Amari.Global Exponential Stability of Multitime Scale Competitive Neural Networks With Nonsmooth Functions.IEEE Transaction on Neural networks,2006,17(5):1152-1164
[93]Andrzej Materka,Shizuo Mizushina.Parametric Signal Restoration Using Artificial Neural Networks.IEEE Transaction on Biomedical Engineering,April,1996,43(4):357-372
[94]张军英，许进．二进前向人工神经网络理论[M]．西安电子科技大学出版社，2001，66-70
[95]向东阳，王公宝，马伟明，等．基于FFT和神经网络的非整数次谐波检测方法．中国电机工程学报，2005，25(9)：35-39
[96]袁继敏，古天祥，徐晨曦．基于空间分割前向竞争网络的信号识别．电子测量与仪器学报，2007，21(3)：15-19
[97]高晋占．微弱信号检测[M]．清华大学出版社，2004，1-3
[98]童勤业，严筱刚，孔军等．“混沌”理论在测量中的应用．电子与信息学报，1999，21(1)：42-44
[99]J.Theiler,S.Eubank.Test for nonlinearity in time series:the method of surrogate data.Physica D,1992,58:77-94
[100]D.T.kaplan,L.Glass.Direct test for determinism in time series.Phys Rev Lett,1992,64(4):427-430
[101]Alexei Potapov,J(u|¨)rgen Kurths.Correlation integral as a tool for distinguishing between Dynamics and statistics in time series data.Physica D,1998,120(3-4):369-385
[102]卫三民，秦荃华，孟晓风．离心机高精度转速控制系统．清华大学学报(自然科学版)，2001，41(3)：51-54
[103]李月，杨宝俊，石要武．B色噪声背景下微弱正弦信号的混沌检测．物理学报，March,2003，52(3)：526-530
[104]Wang Guanyu,Zheng Wei,He Sailing.Estimation of amplitude and phase of a weak signal by using the property of sensitive dependence of initial conditions of a nonlinear oscillator.Signal Processing,2002,82(1):103-105
[105]陈国华，盛昭瀚．基于Lypaunov指数的混沌时间序列识别．系统工厂理论方法应用，2003，12(4)：3 17-320
[106]温权，张勇传，程时杰．辨识混沌时间序列中的确定性．水电能源科学，2001，19(3)：72-75
[107]Y.C.Lai,D.Lerner.Eeffctive scealing regime for computing the correlation dimension from chaotic time series.Physica D:Nonlinear Phonomena,1995,115(1-2):1-18
[108]Y.C.Lai,D.Lerner,R.Hyaden.An upper bound for the proper delay time in chaotic time series analysis.Physics lettA,1996,218(1-2):30-34
[109]I.Matsuba,H.Suyari,S.Weon.Practical chaos time series analysis with financial applications.Proceeding of ICSP.2000,2000,Vol.1:227-265
[110]朱义东，李玉林．基于最大Lypaunov指数方法预测油田产量．西南石油学院学报，2005，27(3)：32-33
[111]田玉楚．混沌时间序列的时滞判定．物理学报，1997，46(3)：442-447
[112]肖方红，阎桂荣，韩宇航．混沌时序相空间重构参数确定的信息论方法．物理学报，2005，54(2)：550-556
[113]Y.C.Tian,F.Gao.Extraction of delay information from chaotic time series based on information entropy.Physica D,1997,108(1-2):113-118
[114]马红光，李夕海．相空间重构中嵌入维和时间延迟的选择．西安交通大学学报，2004，38(4)：335-338
[115]王海燕，盛昭瀚．混沌时间序列相空间重构参数的选取方法．东南大学学报(自然科学版)，2000，30(5)：113-117
[116]D.S.Broomhead,Gregory P.King.Extracting qualitative dynamics from experimental data.Physica D,1986,9:198-208
[117]袁坚，肖先赐．非线性时间序列的高阶奇异谱分析．物理学报，1998，47(6)：897-905
[118]孟庆芳，张强，潘金凤．四阶累积量用于最小嵌入维数估计的新方法．系统工程理论与实践，2005，9：83-88
[119]S.Heidari,C.L.Nikias.Characterizing chaotic attractors using fourth-order off-diagonalcumulant slices.Conference on signal Record of the 27th Asilomar conference Signal systems and computers.Pacific Grove,USA,1993,Vol.1:466-470
[120]L.Y.Cao.Practical method for determining the minimum embedding dimension of a scalar time series.Physica D,1997,110(1-2):43-50
[121]胡岗，萧井华，郑志刚．混沌控制[M]．上海科技教育出版社，2000，177-179
[122]李月，杨宝俊．混沌振子检测引论[M]．电子工业出版社，2004，30-35
[123]洪时中．非线性时间序列分析的最新进展及其在地球科学中的应用前景．地球科学进展，1999，14(6)：559-565
[124]聂春燕，石要武，衣文索．强噪声下利用混沌系统测量频率的新方法．传感器技术，2004，23(3)：57-59
[125]李月，杨宝俊，邓小英，等．谐波信号频率的混沌检测方法．电子与信息学报，2005，27(5)：73 1-733
[126]李月，石要武，王立群，等．纳伏级方波信号的混沌测量方法．计量学报，2003，4(10)：317-320
[127]李月，杨宝俊，石要武．用混沌振子检测淹没在强背景噪声中的方波信号．吉林大学自然科学学报，2001，4(2)：65-68
[128]李月，石要武，马海涛，等．湮没在色噪声背景下微弱方波信号的混沌检测方法．电子学报，2004，32(1)：87-90
[129]梁志国．JJF1057-1998数字存储示波器校准规范[M]．中国计量出版社，1999，8-15
[130]周开利，康耀红．神经网络模型及其MATLAB仿真程序设计[M]．清华大学出版社，2005，69-75
[131]魏海坤．申经网络结构设计的理论与方法[M]．国防工业出版社，2005，111-115
[132]陈曦，李庄伟．关于数字示波器电压测量准确度校准方法研究．上海计量测试，2006，(5)：30-33
[133]沈怀洋．数字示波器的校准．计量技术，2005，(5)：38-40
[134]梁志国，朱济杰．用周期倍差法评价数据采集系统的动特性．中国计量测试学会，1998，1095-1901
[135]W.L.Gans.Dynamic calibration of waveform recorders and oscilloscopes using pulse standards.IEEE Transaction on Instrumentation and Measurement,1990,39(6):952-957
[136]郭献宏．数字示波器垂直精度性能分析．2004，(8)：8-10
[137]梁志国．数字存储示波器计量标准建立中的几个问题．宇航计测技术，1999，19(4)：41-46
[138]林茂六，张喆．高速采样示波器中的时基失真及其估计．计量学报，2004，25(3)：266-269
[139]杜亮．数字示波器计量校准与应用中须注意的若干问题．中国测试技术，2005，31(6)：72-74
[140]庄双勇，朱霞辉．数字存储示波器水平时间扫描因数检定．电子测量技术，2002，(3)：32-33
[141]田晓华，余学锋，赵婉丽．数字存储示波器扫描时间因数的检定．计量与测试技术，2004，(9)：12-13
[142]G.N.Stenbakken,J.P.Deyst.Comparison of time base nonlinearity measurement techniques.IEEE Transaction on Instrumentation and Measurement,1998,47(1):34-39
[143]张莉莉，刘明亮，朱江淼．基于信号重构和系统辨识的上升时间测量．电子测量技术，2006,29(6)：57-59
[144]G.N.Stenbakken,J.P.Deyst.Time-base nonlinearity determination using iterated sine-fit analysis.IEEE Transaction on Instrumentation and Measurement,1998,47(5):1056-1061
[145]赵科佳，刘明亮，郁月华，等．宽带取样示波器上升时间与带宽的转换系数的研究．计量学报，2006，27(2)：160-163
[146]J.Versecht.Broadband sampling oscilloscope characterization with the nose to nose calibration procedure:a theoretical and practical analysis.IEEE Transaction on Instrumentation and Measurement,1995,44(6):991-997
[147]林茂六．高速采样信号数字内插理论与正弦内插算法研究．电子学报，2000，28(12)：8-10
[148]郭伟民，邓晓莉，李莉．关于数字示波器上升时间的探讨．宇航计测技术，2003，23(5)：31-39
[149]余学锋，于洁．示波器上升时间测量的误差分析．宇航计测技术，2002，22(1)：36-41
[150]C.M.Wang,P.D.Hale,K.J.Coakley Least-squares estimation of time-base distortion of sampling oscilloscopes.IEEE Transaction on Instrumentation and Measurement,1999,48(6):1324-1332
[151]马红梅，邓明纫，苏水金，等．70GHz取样示波器上升时间与频响的校准．宇航计测技术，2007，27(5)：6-9
[152]D.Henderson,A.G.Roddie.Calibration of fast sampling oscilloscopes.Meas Sci Technol,1990,(1):673-679
[153]A.Dienstfiey,P.D.Hale,D.A.Keenan,et al.Minimum-phase calibration of sampling oscilloscopes.IEEE Transaction on Microwave Theory And Techniques,2006,54(8):3197-3208
[154]T.S.Clement,P.D.Hale,D.F.Williams,et al.Calibration of sampling oscilloscopes with high-speed photodiodes.IEEE Transaction on Microwave Theory and Techniques,2006,54(8):3173-3181
[155]J.Verspencht.Broadband sampling oscilloscope characterization with the‘nose-to-nose’calibration procedure:a theoretical and practical analysis.IEEE Transaction on Instrumentation and Measurement,1995,44(6):991-997
[156]赵华，林茂六．基于Nose—to—Nose校准法的取样示波器冲激响应的一种算法．电子学报，2003,3 1(3)：365-367
[157]朱江淼，刘明亮，高昀．取样示波器nose-to-nose校准技术中kick-out脉冲的研究．中国计量学院学报，2003，14(3)：185-188
[158]D.R.Larson,N.G.Paulter.The effects of offset voltage on the amplitude and bandwidth of kick-out pulses used in the nose-to-nose sampler impulse response characterization method.IEEE Transaction on Instrumentation and Measurement,2001,50(4):872-876
[159]N.G.Paulter,D.R.Larson.An examination of the spectra of the“kick-out”pulses for a proposed sampling oscilloscope calibration method.IEEE Trans Instrum Meas,2001,50(5):1221-1223
[160]J.Verspecht,K.Rush.Individual characterization of broadband sampling oscilloscopes with a nose-to-nose calibration procedure.IEEE Trans Instrum Meas,1994,43(2):347-354
[161]袁继敏，李小玲，古天祥．宽带取样示波器“NTN”校正和Kick-Out脉冲研究．电子科技大学学报，2007，36(4)：737-739，747