基于小波神经网络的模拟电路故障诊断方法研究
|
摘要
模拟电路故障诊断理论和方法研究是目前研究的热门课题,现代电子技术的发展对模拟电路的测试和故障诊断提出了更高的要求。由于模拟电路故障诊断有其自身的许多困难,因此传统的故障诊断理论和方法在实际工程中很难达到预期的效果。而以神经网络为代表的计算智能技术为模拟电路故障诊断提供了一条有效的途径,受到学术界的广泛关注。本论文以现代测试技术、系统辨识、神经网络和小波分析等理论为基础,深入研究了小波神经网络和多小波神经网络诊断模拟电路故障的方法。本论文的工作主要体现在以下三个方面:
1.研究了基于神经网络的模拟电路故障诊断方法。在分析和阐述神经网络诊断模拟电路故障的系统结构和原理的基础上,研究了BP网络诊断模拟电路故障的方法。针对BP网络易收敛于“局部最小值”这一缺陷,提出了用遗传算法优化BP网络权值的方法,该方法能在较短的时间内得到BP网络的最佳参数。
2.研究了多种小波神经网络诊断模拟电路故障的方法。针对BP网络在模拟电路故障诊断中存在的缺陷:收敛速度慢、容易收敛于“局部最小值”,网络的结构设计仅凭经验以及存在误诊。提出了以下几种小波神经网络诊断模拟电路故障的方法:
1)基于BP小波神经网络的模拟电路故障诊断。首先用具有良好时频局部特性的小波基函数替代传统BP网络的激励函数构造了BP小波神经网络。然后用它诊断模拟电路的故障。诊断结果表明,BP小波神经网络的诊断效果比BP神经网络要好,且收敛速度比BP网络明显加快。
2)基于小波框架神经网络的模拟电路故障诊断。根据离散小波框架理论构造了小波框架神经网络,并用于诊断模拟电路的故障。结果表明,小波框架神经网络的故障诊断率明显比BP网络要高,收敛速度更快,且网络结构设计有理论依据——小波框架理论。
3)基于多分辨小波神经网络的模拟电路故障诊断。从多分辨分析思想出发构造了多分辨小波神经网络。把待诊断电路的样本输入到该网络中进行训练及测试,结果表明:该网络在诊断模拟电路故障过程中,收敛速度快,避免了收敛于“局部最小值”,且网络结构设计有理论依据,更重要的是该网络不仅对已有的故障诊断不存在误诊,而且对新出现的故障也能够全部正确地分类。
3.研究了多分辨多小波神经网络诊断模拟电路故障的方法。和小波相比,多小波同时具有正交性、紧支撑性、对称性和高阶消失矩等优点。本文构造了一种多分辨多小波神经网络,并用于模拟电路的故障诊断。诊断结果表明,该网络保留了多分辨小波神经网络的所有优点,同时克服了多分辨小波神经网络的“维数灾”问题,但良好性质的多小波构造是难点,目前还不成熟。
本论文在小波神经网络诊断模拟电路故障的方法研究中已经取得了一些成果。在接下来的阶段里将继续针对这些工作开展深入的研究。
As modern electronic technology develops very rapidly, the research on the fault diagnosis theories and methods for analog circuits is becoming very popular and also challenging. However, traditional fault diagnosis theories and methods cannot meet actual requirement due to difficulties inherent in analog circuits fault diagnosis. On the other hand, computation intelligence technologies including neural network (NN) method and something else are great interest to researchers, which might provide potential solution for fault diagnosis. In this dissertation, based on the theories of modern test technology, system identification, neural network, wavelet analysis, and so on, the application of wavelet neural network (WNN) and multi-wavelet neural network (MWNN) in fault diagnosis for analog circuits is studied. In short, the main works of this dissertation are as follows:
1. The NN-based fault diagnosis for analog circuits is studied. Firstly, the structure and principle of analog circuits fault diagnosis system based on the NN are analyzed in detail, on which the application of the BP neural network (BPNN) in fault diagnosis is studied. Then, due to the fact that the BPNN usually converges to local minimum, a new way is considered in this dissertation by using the genetic algorithm (GA) to optimize the weights in the hidden layers of the BPNN. The results show that the optimal parameter of the BPNN can be obtained in short time.
2. The research on the different WNNs-based fault diagnosis for analog circuits has been done. Because the traditional BPNN-based fault diagnosis has the following disadvantages such as low convergence rate, easy convergence to local minimum, structure design by experience and false diagnosis. In this dissertation, several WNNs-based fault diagnosis methods for analog circuits are proposed.
1) Analog circuits fault diagnosis method based on the BP wavelet neural network (BPWNN) is proposed. At first, wavelets with good time-frequency localization property instead of the activation functions of the traditional BPNN, is used to construct the BPWNN. Then, it is applied in fault diagnosis for analog circuits. Finally, experimental results indicate that the diagnosis efficiency of the method using the BPWNN is better than that of the method using the BPNN, and that the convergence rate of the former is faster than that of the latter.
2) Analog circuits fault diagnosis method based on the frame wavelet neural network (FWNN) is proposed. Firstly, a FWNN is constructed according to the theory of wavelet frame. Then, it is applied in analog circuits fault diagnosis. Experimental results show that this method is better than the method using the traditional BPNN. And what's more important, wavelet frame theory can be used as theoretical principle in network structure design.
3) Analog circuits fault diagnosis method based on multi-resolution wavelet neural network (MRWNN) is proposed. According to the multi-resolution theory, the MRWNN is constructed in this dissertation. Experimental results show that the method using this MRWNN is characterized by many advantages such as fast convergence rate, avoiding of converging to local minimum, and sound theoretical foundation for structure design. What's more, there is no false diagnosis for existent faults and those new faults can be exactly classified.
3. Analog circuits fault diagnosis method based on multi-resolution multi-wavelet neural network (MRMWNN) is proposed. Compared with conventional wavelet, multi-wavelet is simultaneously orthogonality, compact support, symmetry and vanishing moment. In this dissertation, the MRMWNN is constructed and applied in fault diagnosis for analog circuits. This new fault diagnosis method not only keeps the same advantages as those of the MRWNN, but also overcomes the disadvantage inherent in the MRWNN, i.e., dimension disaster. However, it is not easy to construct good multi-wavelet at present.
In this dissertation, some research works on the application of wavelet neural network in fault diagnosis for analog circuits have been done and some achievements have been made. In the future, further research will continue to be done.
1.研究了基于神经网络的模拟电路故障诊断方法。在分析和阐述神经网络诊断模拟电路故障的系统结构和原理的基础上,研究了BP网络诊断模拟电路故障的方法。针对BP网络易收敛于“局部最小值”这一缺陷,提出了用遗传算法优化BP网络权值的方法,该方法能在较短的时间内得到BP网络的最佳参数。
2.研究了多种小波神经网络诊断模拟电路故障的方法。针对BP网络在模拟电路故障诊断中存在的缺陷:收敛速度慢、容易收敛于“局部最小值”,网络的结构设计仅凭经验以及存在误诊。提出了以下几种小波神经网络诊断模拟电路故障的方法:
1)基于BP小波神经网络的模拟电路故障诊断。首先用具有良好时频局部特性的小波基函数替代传统BP网络的激励函数构造了BP小波神经网络。然后用它诊断模拟电路的故障。诊断结果表明,BP小波神经网络的诊断效果比BP神经网络要好,且收敛速度比BP网络明显加快。
2)基于小波框架神经网络的模拟电路故障诊断。根据离散小波框架理论构造了小波框架神经网络,并用于诊断模拟电路的故障。结果表明,小波框架神经网络的故障诊断率明显比BP网络要高,收敛速度更快,且网络结构设计有理论依据——小波框架理论。
3)基于多分辨小波神经网络的模拟电路故障诊断。从多分辨分析思想出发构造了多分辨小波神经网络。把待诊断电路的样本输入到该网络中进行训练及测试,结果表明:该网络在诊断模拟电路故障过程中,收敛速度快,避免了收敛于“局部最小值”,且网络结构设计有理论依据,更重要的是该网络不仅对已有的故障诊断不存在误诊,而且对新出现的故障也能够全部正确地分类。
3.研究了多分辨多小波神经网络诊断模拟电路故障的方法。和小波相比,多小波同时具有正交性、紧支撑性、对称性和高阶消失矩等优点。本文构造了一种多分辨多小波神经网络,并用于模拟电路的故障诊断。诊断结果表明,该网络保留了多分辨小波神经网络的所有优点,同时克服了多分辨小波神经网络的“维数灾”问题,但良好性质的多小波构造是难点,目前还不成熟。
本论文在小波神经网络诊断模拟电路故障的方法研究中已经取得了一些成果。在接下来的阶段里将继续针对这些工作开展深入的研究。
As modern electronic technology develops very rapidly, the research on the fault diagnosis theories and methods for analog circuits is becoming very popular and also challenging. However, traditional fault diagnosis theories and methods cannot meet actual requirement due to difficulties inherent in analog circuits fault diagnosis. On the other hand, computation intelligence technologies including neural network (NN) method and something else are great interest to researchers, which might provide potential solution for fault diagnosis. In this dissertation, based on the theories of modern test technology, system identification, neural network, wavelet analysis, and so on, the application of wavelet neural network (WNN) and multi-wavelet neural network (MWNN) in fault diagnosis for analog circuits is studied. In short, the main works of this dissertation are as follows:
1. The NN-based fault diagnosis for analog circuits is studied. Firstly, the structure and principle of analog circuits fault diagnosis system based on the NN are analyzed in detail, on which the application of the BP neural network (BPNN) in fault diagnosis is studied. Then, due to the fact that the BPNN usually converges to local minimum, a new way is considered in this dissertation by using the genetic algorithm (GA) to optimize the weights in the hidden layers of the BPNN. The results show that the optimal parameter of the BPNN can be obtained in short time.
2. The research on the different WNNs-based fault diagnosis for analog circuits has been done. Because the traditional BPNN-based fault diagnosis has the following disadvantages such as low convergence rate, easy convergence to local minimum, structure design by experience and false diagnosis. In this dissertation, several WNNs-based fault diagnosis methods for analog circuits are proposed.
1) Analog circuits fault diagnosis method based on the BP wavelet neural network (BPWNN) is proposed. At first, wavelets with good time-frequency localization property instead of the activation functions of the traditional BPNN, is used to construct the BPWNN. Then, it is applied in fault diagnosis for analog circuits. Finally, experimental results indicate that the diagnosis efficiency of the method using the BPWNN is better than that of the method using the BPNN, and that the convergence rate of the former is faster than that of the latter.
2) Analog circuits fault diagnosis method based on the frame wavelet neural network (FWNN) is proposed. Firstly, a FWNN is constructed according to the theory of wavelet frame. Then, it is applied in analog circuits fault diagnosis. Experimental results show that this method is better than the method using the traditional BPNN. And what's more important, wavelet frame theory can be used as theoretical principle in network structure design.
3) Analog circuits fault diagnosis method based on multi-resolution wavelet neural network (MRWNN) is proposed. According to the multi-resolution theory, the MRWNN is constructed in this dissertation. Experimental results show that the method using this MRWNN is characterized by many advantages such as fast convergence rate, avoiding of converging to local minimum, and sound theoretical foundation for structure design. What's more, there is no false diagnosis for existent faults and those new faults can be exactly classified.
3. Analog circuits fault diagnosis method based on multi-resolution multi-wavelet neural network (MRMWNN) is proposed. Compared with conventional wavelet, multi-wavelet is simultaneously orthogonality, compact support, symmetry and vanishing moment. In this dissertation, the MRMWNN is constructed and applied in fault diagnosis for analog circuits. This new fault diagnosis method not only keeps the same advantages as those of the MRWNN, but also overcomes the disadvantage inherent in the MRWNN, i.e., dimension disaster. However, it is not easy to construct good multi-wavelet at present.
In this dissertation, some research works on the application of wavelet neural network in fault diagnosis for analog circuits have been done and some achievements have been made. In the future, further research will continue to be done.
引文
[1]Agrawal V D.Testing in a mixed-signal world.Proceedings of Ninth Annual IEEE 1996International ASIC Conference and Exhibit,1996,241-244
[2]Ogg S,Lesage S,Jervis B W,et al.Multiple fault diagnosis in analogue circuits using time domain response features and multilayer perceptrons,IEE Proceedings of Circuits,Devices and Systems,1998,145(4):213-218
[3]Milor L S.A tutorial introduction to research on analog and mixed-signal circuit testing,IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing,1998,45(10):1389-1407
[4]谭阳红.基于小波和神经网络的大规模模拟电路故障诊断研究:[博士学位论文].长沙:湖南大学,2005,1-2
[5]刘瑞文.反馈系统设计的公理化方法与模拟电路故障诊断问题.讲演记录,上海复旦大学,中国人民解放军国防科大情报资料室,1998,39-55
[6]刘勍,张少刚,齐世平.现代模拟电路故障诊断新方法.信息与电子工程,2006,4(6):476-480
[7]谢保川,刘福太.支持向量机在模拟电路故障诊断中的应用.计算机仿真,2006,23(10):167-170
[8]张利,安霆,杨百顺.灰色关联故障诊断方法在模拟电路中的应用研究.中国测试技术,2006,32(4):39-41
[9]袁海英,陈光(礻禹).基于最大故障信息量二元树的模拟电路诊断法.仪器仪表学报,2006,27(12):1679-1682
[10]Feng Li,Peng-Yung Wo.Fault detection for linear analog IC-the method of short-circuit admittance parameters.IEEE Transactions on Circuits and Systems Ⅰ:Fundamental Theory and Applications,2002,49(1):105-108
[11]Roy A,Sunter S,Fudoli A,et al.High accuracy stimulus generation for A/D converter BIST.Proceedings of International Test Conference,2002,36(1):1031-1039
[12]Variyam P N,Chatterjee A.Enhancing test effectiveness for analog circuits using synthesized measurements.Proceedings of 16th IEEE VLSI Test Symposium,1998,132-137
[13]赵建.模拟电路故障诊断的研究.上海海运学院学报,2000,21(3):49-55
[14]杨士元,童诗白.模拟系统的故障诊断与可靠性设计.北京:清华大学出版社,1993,2-3
[15]Berkowitz R S.Condition for network-element-value solvability.IRE Transactions on Circuits Theory,1962,9(1):24-29
[16]Navid N,Willson Jr A.A theory and algorithm for analog circuit fault diagnosis.IEEE Transactions on Circuits and Systems,1979,26(7):440-457
[17]Wu C C,Nakajima K,Wey C L,et al.Analog fault diagnosis with failure bounds.IEEE Transactions on Circuits and Systems,1982,29(5):277-284
[18]Huang Z F,Lin C S,Liu R W.Node fault diagnosis and a design of testability.IEEE Transactions on Circuits and Systems,1983,30(5):257-265
[19]Salama A,Starzyk J,Bandler J.A unified decomposition approach for fault location in large analog circuits.IEEE Transactions on Circuits and Systems,1984,31(7):609-622
[20]Sorsa T,Koivo H N,Koivisto H.Neural network in process fault diagnosis.IEEE Translations on Systems,Man and Cybernetics,1991,21(4):815-825
[21]Spain R,Upadhyaya S.Linear circuit fault diagnosis using neuromorphie analyzers.IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing,1997,44(3):188-196
[22]El-Gamal M A.A knowledge-based approach for fault detection and isolation in analog circuits.International Conference on Neural Networks,1997,Vol.3:1580-1584
[23]Mic'us'ik D,Stopjakova' V,L'Ben'us'kova'.Application of feed-forward artificial neural networks to the identification of defective analog integrated circuits.Neural Computing &Applications,2002,11(1):71-79
[24]Barbara C,Alessandra F,Stefano M,et al.Neural network- based analog fault diagnosis using testability analysis.Neural Computing&Applications,2004,13(4):288-298
[25]Prithviraj K,Alok B,Satyabroto S.Artificial neural-network model-based observers.IEEE Circuits and Devices Magazine,2005,21(4):18-26
[26]Aminian M,Aminian F.Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor.IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing,2000,47(2):151-156
[27]El-Gamal M A.Genetically evolved neural networks for fault classification in analog circuits.Neural Computing&Application,2002,11(2):112-121
[28]Swarup B,Arijit R,Kaushik R.Defect oriented testing of analog circuits using wavelet analysis of dynamic supply current.Journal of Electronic Testing:Theory and Applications,2005,21(2):147-159
[29]Abdennadher S,Chauhan B.Mixed Signal DFT BIST automation using behavioral modeling.Southwest Symposium on Mixed-Signal Design,2001,137-140
[30]Wang P,Yang S Y.A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math.IEEE Translations on Circuits and Systems,2005,52(10):2118-2127
[31]Negrciros M,Carro L,Susin A A.Ultra low cost analog BIST using spectral analysis.2003Proceedings of the 21st VLSI Test Symposium,2003,27:77-82
[32]Halder A,Chatterjee A.Automated test generation and test point selection for specification test of analog circuits.Proceedings of 5th International Symposium on Quality Electronic Design,2004,401-406
[33]Tadeusiewicz M,Halgas,S.Multiple fault diagnosis in analogue circuits.Proceedings of the 2005 European Conference on Circuit Theory and Design,2005,3:205-208
[34]Miura Y.Characteristics of fault diagnosis for analog circuits based on preset test.The 20th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems,2005,21:573-581
[35]Miura Y,Kato J.Fault diagnosis of analog circuits based on adaptive test and output characteris- tics.The 21st IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems,2006,30:410-418
[36]Cabral E F Jr,Teruya M Y,Soria R A B.Using MLPs for fault analysis in analog circuits.Proceedings of the 38th Midwest Symposium on Circuits and Systems,1995,2:1172-1174
[37]Maiden Y,Jervis B W,Fouillat P,et al.Using artificial neural networks or lagrange interpolation to characterize the faults in an analog circuit:an experimental study.IEEE Transactions on Instrumentation and Measurement,1999,48(5):932-938
[38]Fanni A,Giua A,Marchesi M,et al.A neural network diagnosis approach for analog circuits.Applied Intelligence,1999,11(2):169-186
[39]Mohammadi K,Monfared A R M,Nejad A M.Fault diagnosis of analog circuits with tolerances by using RBF and BP neural networks.Student Conference on Research and Development,2002,317-321
[40]Catelani M,Fort A,Nosi G.Application of radial basis function network to the preventive maintenance of electronic analog circuits.Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference,1999,1:510-513
[41]Nissar A I,Upadhyaya S J.Fault diagnosis of mixed signal VLSI systems using artificial neural networks.1999 Southwest Symposium on Mixed-Signal Design,1999,93-98
[42]El-Gamal M A,EI-Yazeed M F Ahn.A combined clustering and neural network approach for analog multiple hard fault classification.Journal of Electronic Testing:Theory and Applications,1999,14(3):207-217
[43]Vidqvist V.Bayesian methods in fault diagnosis and maintenance of production systems.Postgraduate Course in Automation Technology,Autumn 2002,1-12
[44]Guo Z,Zhang X M.On test and characterization of analog linear time-invariant circuits using neural networks.Proceedings of 10th Asian 2001 Test Sympoisium,2001,338-343
[45]El-Yazeed M F Abu.An integrated approach for analog circuit testing using autocorrelation analysis,singular value decomposition and probabilistic neural network.Proceedings of the 15International Conference on Microelectronics,2003,41-45
[46]Aminian M,Aminian F.A comprehensive examination of neural network architectures for analog fault diagnosis.Proceedings of International Joint Conference on Neural Networks,2001,3:2304-2309
[47]Aminian F,Aminian M,Collins H W Jr.Analog fault diagnosis of actual circuits using neural networks.IEEE Transactions on Instrumentation and Measurement,2002,51(3):544-550
[48]Contu S,Fanni A,Marchesi M,et al.Wavelet analysis for diagnostic problems.8th Mediterranean Electrotechnical Conference,1996,3:1571-1574
[49]Yu S,Jervis B W,Eckersall K R,et al.Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults.Electronics Letters,1994,30(9):695-696
[50]Ashouri M-R.Fault detection of analog circuits using neural networks and Monte-Carlo analysis.Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems,2001,2:700-704
[51]Stopjakova V,Malosek P,Micusik D,et al,Classification of defective analog integrated circuits using artificial neural networks.Journal of Electronic Testing:Theory and Applications,Kluwer Academic Publishers,2004,20(1):25-37
[52]Grzechca D,Rutkowski J.Usc of neural network and fuzzy logic to time domain analog tasting.Proceedings of the 9th International Conference on Neural Information Processing,2002,5:2601-2604
[53]Torralba A,Chavcz J,Franquelo L G.Fault detection and classification of analog circuits by means of fuzzy logic-based techniques.IEEE International Symposium on Circuits and Systems,1995,3:1828-1831
[54]Starzyk J A,Liu D.A decomposition method for analog fault location.IEEE International Symposium on Circuits and Systems,2002,3:157-160
[55]Iordache M,Dumitriu L.Efficient decomposition techniques for symbolic analysis of large-scale analog circuits by state variable method.Analog Integrated Circuits and Signal Processing,2004,40(3):235-253
[56]Chung Kin Ho,Shepherd P R,Eerhardt F,et al.Hierarchical fault diagnosis of analog integrated circuits.IEEE Transactions on Circuits and Systems I:Fundamental Theory and Applications,2001,48(8):921-929
[57]Voorakaranam R,Chatterjee A.Hierarchical test generation for analog circuits using incremental test development.17th IEEE Proceedings of VLSI Test Symposium,1999,296-301
[58]Soma M,Huynh S,Zhang J,et al.Hierarchical ATPG for analog circuits and systems.IEEE Design &Test of Computers,2001,18(1):72-81
[59]Filippotti F,Martelli M.Symbolic techniques addressed to the fault diagnosis of large linear electric circuits.8th Mediterranean Electrotechnical Conference,1996,3:1279-1282
[60]Shi C-J R,Xiang-Dong Tan.Canonical symbolic analysis of large analog circuits with determinant decision diagrams.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2000,19(I):1-18
[61]Somayajula S S.A neural network approach to hierarchical analog fault diagnosis.Proceedings of IEEE Systems Readiness Technology Conference,1993,699-706
[62]雍正正.模拟电路故障诊断:模糊故障字典法.数据采集与处理,1998,13(3):210-213
[63]陈圣俭,洪炳熔,王月芳等.可诊断容差模拟电路的软故障的新故障字典法.电子学报,2000,28(2):127-129
[64]潭阳红,何怡刚.模拟电路故障诊断的新故障字典法.微电子学,2001,31(4):252-254
[65]刘宇东,赵宏革.用故障字典法完成模拟电路软故障诊断的研究.世界海运,2002,26(2):40-41
[66]彭敏放,何怡刚.一种新的容差模拟电路故障诊断屏蔽字典法.微电子学与计算机,2004,21(6):199-202
[67]彭敏放,何怡刚.容差模拟电路的模糊软故障字典法诊断.湖南大学学报(自然科学版),2005,32(1):25-28
[68]张伟,许爱强,陈振林.模拟电路节点电压灵敏度权序列故障字典法.电子测量与仪器学报,2006,20(4):46-49
[69]董晨皓,林争辉,秦建业.模拟电路故障诊断的辨识法及其方程.上海交通大学学报,1995,29(1):138-143
[70]师宇杰,张耀升.模拟电路故障诊断的Ⅷ验证算法.天津大学学报,1998,31(5):600-605
[71]凌燮亭.非线性电路的故障分析.电子学报,1981,9(1):70-77
[72]凌燮亭.模拟电路中有限故障数的预测-判定分析方法.电子学报,1982,3(5):37-45
[73]凌燮亭.电路增量分析的伴随电路方法.电子学报,1985,6(13):72-79
[74]孙义闯,林在旭.非线性电路故障诊断的似故障增量电路法.电子学报,1987,15(5):82-88
[75]吴耀,童诗白.线性电路K故障诊断法的有效范围.电子科学学刊,1989,2(11):113-120
[76]焦李成.非线性电路和系统的灵敏度分析-非线性传递函数法.电子科学学刊,1989,11(2):129-136
[77]林争辉,武强.非线性电路故障诊断了理论和算法.上海交通大学学报,1995,29(1):16-22
[78]殷时蓉,陈光礻禹,谢永乐.Volterra核的测量及在非线性模拟电路测试中的应用.控制与决策,2006,21(10):1134-1137
[79]马红光,韩崇昭,孔祥玉等.基于Lypunov指数的非线性模拟电路故障诊断方法.电路与系统学报,2004,19(4):71-75
[80]谢宏.何怡刚.非线性模拟动态电路故障诊断的频域方法.仪器仪表学报,2006,27(5):512-514
[81]雷勇,陈光礻禹,谢永乐等.基于导纳矩阵分解的非线性电路故障诊断.仪器仪表学报,2005,26(12):1282-1285
[82]沈谦,张良震.BP网络改进及在模拟电路故障诊断中的应用.微电子与计算机,1996,13(1):37-40
[83]尉乃红,杨士元,童诗白.基于BP网络的线性电路故障诊断.计算机学报,1997,20(4):360-366
[84]何怡刚,罗先觉,邱关源.基于神经网络的线性电路故障诊断非线性L1范数优化方法.电子测量与仪器学报,1998,12(1):18-22
[85]何怡刚.非线性容差模拟电阻电路故障诊断神经网络方法.固体电子学研究发展,2002,22(3):297-299
[86]彭翀,樊锐,刘强.基于人工神经网络的电路故障诊断专家系统.系统工程与电子技术,2002,24(10):116-119
[87]陶锋,李春明,刘利强.基于模糊神经网络的模拟电路诊断系统的研究.内蒙古工业大学学报(自然科学版),2003,22(3):218-222
[88]梁戈超,何怡刚,朱彦卿.基于模糊神经网络融合遗传算法的模拟电路故障诊断法.电路与系统学报,2004,9(2):54-57.
[89]朱大奇,于盛林.电子电路故障诊断的神经网络数据融合算法.东南大学学报(自然科学版,2001,31(6):87-90
[90]谢宏,何怡刚,吴杰.小波变换在模拟电路故障诊断中的应用.数学理论与应用,2001,21(3):96-98
[91]李楠.基于模拟系统响应的小波变换故障诊断方法的仿真研究.计算机仿真,2001,18(6):7-13
[92]王淑娟,陈博,赵国良.基于小波包变换预处理的模拟电路故障诊断方法.电工技术学报,2003,18(4):118-122
[93]谢宏,何怡刚,吴杰.基于小波-神经网络模拟电路故障诊断方法的研究.仪器仪表学报,2004,25(5):672-675
[94]王承,陈光礻禹,谢永乐.基于小波-神经网络的模拟电路故障诊断.系统仿真学报,2005,17(8):1936-1938
[95]郑致刚,胡云安.小波神经网络在模拟电路故障诊断中的应用.微计算机信息,2006,22(16):206-208
[96]王承,陈光礻禹,谢永乐.基于小波-神经网络的模拟电路I_(DDT)故障诊断.仪器仪表学报,2005,26(11):1106-1108
[97]王承,陈光福,谢永乐.基于主元分析与神经网络的模拟电路故障诊断.电子测量与仪器学报,2005,19(5):14-17
[98]韩宝如,孟玲玲.紧致型小波网络在模拟电路故障诊断中的应用.现代电子技术,2006,231(16):145-149
[99]李春明,王勇.基于小波神经网络的模拟电路故障诊断.微计算机信息,2007,23(1):204-205
[100]吕敬祥,彭敏放,曹铁军.遗传BP网络在模拟电路故障诊断的应用.自动化与仪表,2005,20(1):4-6
[101]袁慧梅,李锦萍.模拟电路故障诊断新方法.航空精密制造技术,2002,38(2):20-23
[102]谭阳红,何怡刚,陈洪云等.大规模电路故障诊断神经网络方法.电路与系统学报,2001,6(4):25-28
[103]李杏梅,张友纯.大规模电路故障诊断BP神经网络新方法.自动化与仪器仪表,2002,104(6):56-60
[104]谭阳红,何怡刚,陈洪云.大规模电路故障诊断的集团法.电子测量与仪器学报,2003,17(4):57-62
[105]He Y G,Tan Y H,Sun Y C.Wavelet neural network approach for fault diagnosis of analogue circuits.IEE Proceedings Circuits Devices Systems,2004,151(4):379-384
[106]Yuan H Y,Chert G J,Shi S B,et al.Research on fault diagnosis in analog circuit based on wavelet-neural network.IEEE Proeedings of the 6th World Congress on Intelligent Control and Automation,Dalian,China,2006,4:2659-2662
[107]Yin S R,Chen G J,Xie Y L.Wavelet neural network based fault diagnosis in nonlinear analog circuits.Journal of Systems Engineering and Electronics,2006,17(3):521-526
[108]Szu H H,Teller B A,Kadambe S L.Neural network adaptive wavelets for signal representation and classification.Optical Engineering,1992,31(9):1907-1916
[109]Baskshi B R,Stephanopoulous G.Wave-net:a multiresolution,hierarchical neural network with localized learning.AICHE Journal,1993,39(1):57-81
[110]Boubez T I,Peskin R L,Wavelet neural networks and receptive field partitioning.IEEE International Conference on Neural Networks,1993,3:1544-1549
[111]徐晓霞,陈涛,王晓升.基于正交最小二乘算法的小波神经网络.电子学报,1998,26(10):115-117
[112]Rying E A,Bilbro G L,Lu J C.Focused local learning with wavelet neural networks.IEEE Transactions on Neural Networks,2002,13(2):304-319
[113]Zhang Q H,Benveniste A.Wavelet networks.IEEE Transactions on Neural Networks.1992,3(6):889-898
[114]Pati Y C,Krishnaprasad P S.Analysis and synthesis of fcedforward neural networks using discrete affine wavelet transformafions.IEEE Transactions on Neural Networks,1993,4(1):73-85
[115]Zhang J,Walter GG.MiaoYubo,et al.Wavelet neural networks for function learning.IEEE Transactions on Signal Processing,1995,43(6):1485-1497
[116]Delyon B,Juditaky A,Benveniste A.Accuracy analysis for wavelet approximations.IEEE Transactions on Neural Networks,1995,6(2):332-348
[117]Li X L,Gao X P.Multiwavelet neural network:a novel model.2003 IEEE International Conference on Systems,Man and Cybernetics,Crystal City Hyatt Regency Washington D.C,USA,2003,3:2629-2632
[118]Gao X P,Xiao F.Multiwavelet networks for prediction of chaotic time series.2004 IEEE International Conference on Systems,Man and Cybernetics,2004,4:3328-3332
[119]Liu Z G.Multiwavelet neural networks construction study.2005 International Symposium on Signal,Circuits and Systems,2005,2:789-792
[120]Starzyk J A,EI-Gamal M A.Artificial neural network for testing analog circuits.IEEE International Symposium on Circuits and Systems,1990,3:1851-1854.
[121]He Y G,Deng Y,Sun Y C.Fault diagnosis of analog circuits with tolerances using artificial neural networks.Proceedings IEEE APCCAS,Tianjin,China,2000,292-295
[122]He Y G,Sun Y C.Neural network based Li-norm optimization approach for fault diagnosis of nonlinear circuits with tolerances.IEEE Circuits Device Systems,2001,148(2),223-228
[123]Catelani M,Fort A.Soft fault detection and isolation in analog circuits:some results and a comparison between a fuzzy approach and radial basis-function network.IEEE Transactions on Instrumentation and Measurement,2002,51(2):196-202
[124]Guo Q J,Yu H B,Xu A D.Wavelet fuzzy neural network for fault diagnosis.Proceedings 2005International Conference on Communications,Circuits and Systems,2005,2:27-30
[125]Tong D W,Walther E,Zalondek K C.Diagnosing an analog feedback system using model-based reasoning.Proceedings of the Annual AI Systems in Government Conference,1989,290-295
[126]Farmi A,Giua A,Sandoli E.Neural networks for multiple fault diagnosis in analog circuits.The IEEE International Workshop on Defect and Fault Tolerance in VLSI Systems,1993,303-310
[127]Ahmed S,Cheung P Y K.Analog fault diagnosis-A practical approach.1994 IEEE International Symposium on Circuits and Systems,1994,1:351-354
[128]Buntine W.A guide to the literature on learning probabilistic networks from data.IEEE Transactions on Knowledge and Data Engineering,1996,8(2):195-210
[129]Kirkland L V,Wright R G.Using neural networks to solve testing problems.IEEE Aerospace and Electronic Systens Magazine,1997,12(8):36-40
[130]Catelani M,Giraldi S.Fault diagnosis of analog circuits with model-based technique.1998Instrumentation and Measurement Technology Conference,1998,1:501-504
[131]Shojaei M,Sharif-Bakhtiar M.Automatic design of analog circuits based on behavioral model.IEEE Canadian Conference on Electrical and Computer Engineering,1998,1:145-148
[132]袁海英,陈光礻禹,谢永乐等.基于非线性电路频域核估计和神经网络的故障诊断.控制与决策,2007,22(4):473-476
[133]Ozawa T,Yamada M,Salama A.Diagnosability in the decomposition approach for fault location in large Analog circuits.IEEE Transactions on Circuits and Systems,1985,32(4):415-416
[134]Yuan H Y,Chen G J,Xie Y L.Feature extraction based on neural network in analog circuit fault diagnosis.Journal of Systems Engineering and Electronics,2007,18(2):434-437
[135]袁海英,陈光礻禹,谢永乐.故障诊断中基于神经网络的特征提取方法研究.仪器仪表学报,2007,28(1):90-94
[136]陈晓娟.模拟电路神经网络故障诊断方法研究:[博士学位论文].吉林:吉林大学,2006,35-37
[137]Martin T.Hagan,Howard B.Demuth,Mark H.Bcale.神经网络设计(戴葵,译者).北京:机械工业出版社,2002,232-235
[138]王承.基于神经网络的模拟电路故障诊断方法研究:[博士学位论文].成都:电子科技大学,2005,25
[139]毕天姝,倪以信,吴复立等.基于混合神经网络和遗传算法的故障诊断系统.现代电力,2005,22(1):31-36
[140]苗君明,佟刚,杨者青.基于遗传算法的神经网络优化.沈阳航空工业学院学报,2005,22(3):30-32
[141]梁化楼,戴贵亮.人工神经网络与遗传算法的结合进展及展望.电子学报,1995,23(10):194-200
[142]熊棱,赵明旺.基于遗传算法的BP网络全局收敛的混合智能学习算法.武汉科技大学学报(自然科学版),2000,23(2):183-186.
[143]史永胜,宋云霞.基于遗传算法与BP神经网络的故障诊断模型.计算机工程,2004,30(14):125-127
[144]田旭光,宋彤,刘宇新.结合遗传算法优化BP神经网络的结构和参数.计算机应用与软件,2004,21(6):69-71
[145]闫河,唐德东,黄扬帆等.基于遗传算法的BP网络快速自适应性算法.计算机仿真,2005,22(1):103-106
[146]金瑜,陈光礻禹,边海龙.基于移民算子遗传BP神经网络的模拟电路故障诊断.电子测量与仪器学报,2007,21(1):20-24
[147]陈伦军,罗延科,陈海红.机械优化设计遗传算法.北京:机械工业出版社,2005,50-52
[148]Kaminska B,Arabi K,Bell I,et al.Analog and mixed signal benchmark circuits first release.proceedings of International Test Conference,1997,183-190
[149]金瑜,陈光礻禹,刘红.基于BP小波神经网络的模拟电路故障诊断.测控技术,2007,26(7):64-66
[150]金瑜,陈光礻禹,刘红.基于小波神经网络的模拟电路故障诊断方法.仪器仪表学报,2007,28(9):1600-1604
[151]金瑜,陈光礻禹,刘红.基于非张量积小波网络的模拟电路故障诊断.仪器仪表学报,已录用
[152]Kovaǒevió J,Martin V.Nonseparable Multidimensional Perfect Reconstruction Filter Bank and Wavelet Bases for R~n.IEEE Transaction on Information Theory,1992,38(2):533-555
[153]高西奇,甘露,邹采荣.多小波变换的理论及其在图象处理中的应用.通信学报,1999,20(11):55-66
[154]程正兴,张玲玲.多小波分析及应用.工程数学学报,2001,18(1):99-107
[155]Jiao L C,Pan J,Fang Y W.Multiwavelet neural network and its approximation properties.IEEE Transactions on Neural Networks,2001,12(5):1060-1066
[156]金瑜,陈光礻禹,刘红.用多小波神经网络诊断模拟电路故障的方法.计算机辅助设计与图形学学报,2007,19(10):1247-1251
[157]金瑜,陈光礻禹,刘红.模拟电路故障诊断的新方法.仪器仪表学报,2007,28(10):1870-1873
[2]Ogg S,Lesage S,Jervis B W,et al.Multiple fault diagnosis in analogue circuits using time domain response features and multilayer perceptrons,IEE Proceedings of Circuits,Devices and Systems,1998,145(4):213-218
[3]Milor L S.A tutorial introduction to research on analog and mixed-signal circuit testing,IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing,1998,45(10):1389-1407
[4]谭阳红.基于小波和神经网络的大规模模拟电路故障诊断研究:[博士学位论文].长沙:湖南大学,2005,1-2
[5]刘瑞文.反馈系统设计的公理化方法与模拟电路故障诊断问题.讲演记录,上海复旦大学,中国人民解放军国防科大情报资料室,1998,39-55
[6]刘勍,张少刚,齐世平.现代模拟电路故障诊断新方法.信息与电子工程,2006,4(6):476-480
[7]谢保川,刘福太.支持向量机在模拟电路故障诊断中的应用.计算机仿真,2006,23(10):167-170
[8]张利,安霆,杨百顺.灰色关联故障诊断方法在模拟电路中的应用研究.中国测试技术,2006,32(4):39-41
[9]袁海英,陈光(礻禹).基于最大故障信息量二元树的模拟电路诊断法.仪器仪表学报,2006,27(12):1679-1682
[10]Feng Li,Peng-Yung Wo.Fault detection for linear analog IC-the method of short-circuit admittance parameters.IEEE Transactions on Circuits and Systems Ⅰ:Fundamental Theory and Applications,2002,49(1):105-108
[11]Roy A,Sunter S,Fudoli A,et al.High accuracy stimulus generation for A/D converter BIST.Proceedings of International Test Conference,2002,36(1):1031-1039
[12]Variyam P N,Chatterjee A.Enhancing test effectiveness for analog circuits using synthesized measurements.Proceedings of 16th IEEE VLSI Test Symposium,1998,132-137
[13]赵建.模拟电路故障诊断的研究.上海海运学院学报,2000,21(3):49-55
[14]杨士元,童诗白.模拟系统的故障诊断与可靠性设计.北京:清华大学出版社,1993,2-3
[15]Berkowitz R S.Condition for network-element-value solvability.IRE Transactions on Circuits Theory,1962,9(1):24-29
[16]Navid N,Willson Jr A.A theory and algorithm for analog circuit fault diagnosis.IEEE Transactions on Circuits and Systems,1979,26(7):440-457
[17]Wu C C,Nakajima K,Wey C L,et al.Analog fault diagnosis with failure bounds.IEEE Transactions on Circuits and Systems,1982,29(5):277-284
[18]Huang Z F,Lin C S,Liu R W.Node fault diagnosis and a design of testability.IEEE Transactions on Circuits and Systems,1983,30(5):257-265
[19]Salama A,Starzyk J,Bandler J.A unified decomposition approach for fault location in large analog circuits.IEEE Transactions on Circuits and Systems,1984,31(7):609-622
[20]Sorsa T,Koivo H N,Koivisto H.Neural network in process fault diagnosis.IEEE Translations on Systems,Man and Cybernetics,1991,21(4):815-825
[21]Spain R,Upadhyaya S.Linear circuit fault diagnosis using neuromorphie analyzers.IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing,1997,44(3):188-196
[22]El-Gamal M A.A knowledge-based approach for fault detection and isolation in analog circuits.International Conference on Neural Networks,1997,Vol.3:1580-1584
[23]Mic'us'ik D,Stopjakova' V,L'Ben'us'kova'.Application of feed-forward artificial neural networks to the identification of defective analog integrated circuits.Neural Computing &Applications,2002,11(1):71-79
[24]Barbara C,Alessandra F,Stefano M,et al.Neural network- based analog fault diagnosis using testability analysis.Neural Computing&Applications,2004,13(4):288-298
[25]Prithviraj K,Alok B,Satyabroto S.Artificial neural-network model-based observers.IEEE Circuits and Devices Magazine,2005,21(4):18-26
[26]Aminian M,Aminian F.Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor.IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing,2000,47(2):151-156
[27]El-Gamal M A.Genetically evolved neural networks for fault classification in analog circuits.Neural Computing&Application,2002,11(2):112-121
[28]Swarup B,Arijit R,Kaushik R.Defect oriented testing of analog circuits using wavelet analysis of dynamic supply current.Journal of Electronic Testing:Theory and Applications,2005,21(2):147-159
[29]Abdennadher S,Chauhan B.Mixed Signal DFT BIST automation using behavioral modeling.Southwest Symposium on Mixed-Signal Design,2001,137-140
[30]Wang P,Yang S Y.A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math.IEEE Translations on Circuits and Systems,2005,52(10):2118-2127
[31]Negrciros M,Carro L,Susin A A.Ultra low cost analog BIST using spectral analysis.2003Proceedings of the 21st VLSI Test Symposium,2003,27:77-82
[32]Halder A,Chatterjee A.Automated test generation and test point selection for specification test of analog circuits.Proceedings of 5th International Symposium on Quality Electronic Design,2004,401-406
[33]Tadeusiewicz M,Halgas,S.Multiple fault diagnosis in analogue circuits.Proceedings of the 2005 European Conference on Circuit Theory and Design,2005,3:205-208
[34]Miura Y.Characteristics of fault diagnosis for analog circuits based on preset test.The 20th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems,2005,21:573-581
[35]Miura Y,Kato J.Fault diagnosis of analog circuits based on adaptive test and output characteris- tics.The 21st IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems,2006,30:410-418
[36]Cabral E F Jr,Teruya M Y,Soria R A B.Using MLPs for fault analysis in analog circuits.Proceedings of the 38th Midwest Symposium on Circuits and Systems,1995,2:1172-1174
[37]Maiden Y,Jervis B W,Fouillat P,et al.Using artificial neural networks or lagrange interpolation to characterize the faults in an analog circuit:an experimental study.IEEE Transactions on Instrumentation and Measurement,1999,48(5):932-938
[38]Fanni A,Giua A,Marchesi M,et al.A neural network diagnosis approach for analog circuits.Applied Intelligence,1999,11(2):169-186
[39]Mohammadi K,Monfared A R M,Nejad A M.Fault diagnosis of analog circuits with tolerances by using RBF and BP neural networks.Student Conference on Research and Development,2002,317-321
[40]Catelani M,Fort A,Nosi G.Application of radial basis function network to the preventive maintenance of electronic analog circuits.Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference,1999,1:510-513
[41]Nissar A I,Upadhyaya S J.Fault diagnosis of mixed signal VLSI systems using artificial neural networks.1999 Southwest Symposium on Mixed-Signal Design,1999,93-98
[42]El-Gamal M A,EI-Yazeed M F Ahn.A combined clustering and neural network approach for analog multiple hard fault classification.Journal of Electronic Testing:Theory and Applications,1999,14(3):207-217
[43]Vidqvist V.Bayesian methods in fault diagnosis and maintenance of production systems.Postgraduate Course in Automation Technology,Autumn 2002,1-12
[44]Guo Z,Zhang X M.On test and characterization of analog linear time-invariant circuits using neural networks.Proceedings of 10th Asian 2001 Test Sympoisium,2001,338-343
[45]El-Yazeed M F Abu.An integrated approach for analog circuit testing using autocorrelation analysis,singular value decomposition and probabilistic neural network.Proceedings of the 15International Conference on Microelectronics,2003,41-45
[46]Aminian M,Aminian F.A comprehensive examination of neural network architectures for analog fault diagnosis.Proceedings of International Joint Conference on Neural Networks,2001,3:2304-2309
[47]Aminian F,Aminian M,Collins H W Jr.Analog fault diagnosis of actual circuits using neural networks.IEEE Transactions on Instrumentation and Measurement,2002,51(3):544-550
[48]Contu S,Fanni A,Marchesi M,et al.Wavelet analysis for diagnostic problems.8th Mediterranean Electrotechnical Conference,1996,3:1571-1574
[49]Yu S,Jervis B W,Eckersall K R,et al.Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults.Electronics Letters,1994,30(9):695-696
[50]Ashouri M-R.Fault detection of analog circuits using neural networks and Monte-Carlo analysis.Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems,2001,2:700-704
[51]Stopjakova V,Malosek P,Micusik D,et al,Classification of defective analog integrated circuits using artificial neural networks.Journal of Electronic Testing:Theory and Applications,Kluwer Academic Publishers,2004,20(1):25-37
[52]Grzechca D,Rutkowski J.Usc of neural network and fuzzy logic to time domain analog tasting.Proceedings of the 9th International Conference on Neural Information Processing,2002,5:2601-2604
[53]Torralba A,Chavcz J,Franquelo L G.Fault detection and classification of analog circuits by means of fuzzy logic-based techniques.IEEE International Symposium on Circuits and Systems,1995,3:1828-1831
[54]Starzyk J A,Liu D.A decomposition method for analog fault location.IEEE International Symposium on Circuits and Systems,2002,3:157-160
[55]Iordache M,Dumitriu L.Efficient decomposition techniques for symbolic analysis of large-scale analog circuits by state variable method.Analog Integrated Circuits and Signal Processing,2004,40(3):235-253
[56]Chung Kin Ho,Shepherd P R,Eerhardt F,et al.Hierarchical fault diagnosis of analog integrated circuits.IEEE Transactions on Circuits and Systems I:Fundamental Theory and Applications,2001,48(8):921-929
[57]Voorakaranam R,Chatterjee A.Hierarchical test generation for analog circuits using incremental test development.17th IEEE Proceedings of VLSI Test Symposium,1999,296-301
[58]Soma M,Huynh S,Zhang J,et al.Hierarchical ATPG for analog circuits and systems.IEEE Design &Test of Computers,2001,18(1):72-81
[59]Filippotti F,Martelli M.Symbolic techniques addressed to the fault diagnosis of large linear electric circuits.8th Mediterranean Electrotechnical Conference,1996,3:1279-1282
[60]Shi C-J R,Xiang-Dong Tan.Canonical symbolic analysis of large analog circuits with determinant decision diagrams.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2000,19(I):1-18
[61]Somayajula S S.A neural network approach to hierarchical analog fault diagnosis.Proceedings of IEEE Systems Readiness Technology Conference,1993,699-706
[62]雍正正.模拟电路故障诊断:模糊故障字典法.数据采集与处理,1998,13(3):210-213
[63]陈圣俭,洪炳熔,王月芳等.可诊断容差模拟电路的软故障的新故障字典法.电子学报,2000,28(2):127-129
[64]潭阳红,何怡刚.模拟电路故障诊断的新故障字典法.微电子学,2001,31(4):252-254
[65]刘宇东,赵宏革.用故障字典法完成模拟电路软故障诊断的研究.世界海运,2002,26(2):40-41
[66]彭敏放,何怡刚.一种新的容差模拟电路故障诊断屏蔽字典法.微电子学与计算机,2004,21(6):199-202
[67]彭敏放,何怡刚.容差模拟电路的模糊软故障字典法诊断.湖南大学学报(自然科学版),2005,32(1):25-28
[68]张伟,许爱强,陈振林.模拟电路节点电压灵敏度权序列故障字典法.电子测量与仪器学报,2006,20(4):46-49
[69]董晨皓,林争辉,秦建业.模拟电路故障诊断的辨识法及其方程.上海交通大学学报,1995,29(1):138-143
[70]师宇杰,张耀升.模拟电路故障诊断的Ⅷ验证算法.天津大学学报,1998,31(5):600-605
[71]凌燮亭.非线性电路的故障分析.电子学报,1981,9(1):70-77
[72]凌燮亭.模拟电路中有限故障数的预测-判定分析方法.电子学报,1982,3(5):37-45
[73]凌燮亭.电路增量分析的伴随电路方法.电子学报,1985,6(13):72-79
[74]孙义闯,林在旭.非线性电路故障诊断的似故障增量电路法.电子学报,1987,15(5):82-88
[75]吴耀,童诗白.线性电路K故障诊断法的有效范围.电子科学学刊,1989,2(11):113-120
[76]焦李成.非线性电路和系统的灵敏度分析-非线性传递函数法.电子科学学刊,1989,11(2):129-136
[77]林争辉,武强.非线性电路故障诊断了理论和算法.上海交通大学学报,1995,29(1):16-22
[78]殷时蓉,陈光礻禹,谢永乐.Volterra核的测量及在非线性模拟电路测试中的应用.控制与决策,2006,21(10):1134-1137
[79]马红光,韩崇昭,孔祥玉等.基于Lypunov指数的非线性模拟电路故障诊断方法.电路与系统学报,2004,19(4):71-75
[80]谢宏.何怡刚.非线性模拟动态电路故障诊断的频域方法.仪器仪表学报,2006,27(5):512-514
[81]雷勇,陈光礻禹,谢永乐等.基于导纳矩阵分解的非线性电路故障诊断.仪器仪表学报,2005,26(12):1282-1285
[82]沈谦,张良震.BP网络改进及在模拟电路故障诊断中的应用.微电子与计算机,1996,13(1):37-40
[83]尉乃红,杨士元,童诗白.基于BP网络的线性电路故障诊断.计算机学报,1997,20(4):360-366
[84]何怡刚,罗先觉,邱关源.基于神经网络的线性电路故障诊断非线性L1范数优化方法.电子测量与仪器学报,1998,12(1):18-22
[85]何怡刚.非线性容差模拟电阻电路故障诊断神经网络方法.固体电子学研究发展,2002,22(3):297-299
[86]彭翀,樊锐,刘强.基于人工神经网络的电路故障诊断专家系统.系统工程与电子技术,2002,24(10):116-119
[87]陶锋,李春明,刘利强.基于模糊神经网络的模拟电路诊断系统的研究.内蒙古工业大学学报(自然科学版),2003,22(3):218-222
[88]梁戈超,何怡刚,朱彦卿.基于模糊神经网络融合遗传算法的模拟电路故障诊断法.电路与系统学报,2004,9(2):54-57.
[89]朱大奇,于盛林.电子电路故障诊断的神经网络数据融合算法.东南大学学报(自然科学版,2001,31(6):87-90
[90]谢宏,何怡刚,吴杰.小波变换在模拟电路故障诊断中的应用.数学理论与应用,2001,21(3):96-98
[91]李楠.基于模拟系统响应的小波变换故障诊断方法的仿真研究.计算机仿真,2001,18(6):7-13
[92]王淑娟,陈博,赵国良.基于小波包变换预处理的模拟电路故障诊断方法.电工技术学报,2003,18(4):118-122
[93]谢宏,何怡刚,吴杰.基于小波-神经网络模拟电路故障诊断方法的研究.仪器仪表学报,2004,25(5):672-675
[94]王承,陈光礻禹,谢永乐.基于小波-神经网络的模拟电路故障诊断.系统仿真学报,2005,17(8):1936-1938
[95]郑致刚,胡云安.小波神经网络在模拟电路故障诊断中的应用.微计算机信息,2006,22(16):206-208
[96]王承,陈光礻禹,谢永乐.基于小波-神经网络的模拟电路I_(DDT)故障诊断.仪器仪表学报,2005,26(11):1106-1108
[97]王承,陈光福,谢永乐.基于主元分析与神经网络的模拟电路故障诊断.电子测量与仪器学报,2005,19(5):14-17
[98]韩宝如,孟玲玲.紧致型小波网络在模拟电路故障诊断中的应用.现代电子技术,2006,231(16):145-149
[99]李春明,王勇.基于小波神经网络的模拟电路故障诊断.微计算机信息,2007,23(1):204-205
[100]吕敬祥,彭敏放,曹铁军.遗传BP网络在模拟电路故障诊断的应用.自动化与仪表,2005,20(1):4-6
[101]袁慧梅,李锦萍.模拟电路故障诊断新方法.航空精密制造技术,2002,38(2):20-23
[102]谭阳红,何怡刚,陈洪云等.大规模电路故障诊断神经网络方法.电路与系统学报,2001,6(4):25-28
[103]李杏梅,张友纯.大规模电路故障诊断BP神经网络新方法.自动化与仪器仪表,2002,104(6):56-60
[104]谭阳红,何怡刚,陈洪云.大规模电路故障诊断的集团法.电子测量与仪器学报,2003,17(4):57-62
[105]He Y G,Tan Y H,Sun Y C.Wavelet neural network approach for fault diagnosis of analogue circuits.IEE Proceedings Circuits Devices Systems,2004,151(4):379-384
[106]Yuan H Y,Chert G J,Shi S B,et al.Research on fault diagnosis in analog circuit based on wavelet-neural network.IEEE Proeedings of the 6th World Congress on Intelligent Control and Automation,Dalian,China,2006,4:2659-2662
[107]Yin S R,Chen G J,Xie Y L.Wavelet neural network based fault diagnosis in nonlinear analog circuits.Journal of Systems Engineering and Electronics,2006,17(3):521-526
[108]Szu H H,Teller B A,Kadambe S L.Neural network adaptive wavelets for signal representation and classification.Optical Engineering,1992,31(9):1907-1916
[109]Baskshi B R,Stephanopoulous G.Wave-net:a multiresolution,hierarchical neural network with localized learning.AICHE Journal,1993,39(1):57-81
[110]Boubez T I,Peskin R L,Wavelet neural networks and receptive field partitioning.IEEE International Conference on Neural Networks,1993,3:1544-1549
[111]徐晓霞,陈涛,王晓升.基于正交最小二乘算法的小波神经网络.电子学报,1998,26(10):115-117
[112]Rying E A,Bilbro G L,Lu J C.Focused local learning with wavelet neural networks.IEEE Transactions on Neural Networks,2002,13(2):304-319
[113]Zhang Q H,Benveniste A.Wavelet networks.IEEE Transactions on Neural Networks.1992,3(6):889-898
[114]Pati Y C,Krishnaprasad P S.Analysis and synthesis of fcedforward neural networks using discrete affine wavelet transformafions.IEEE Transactions on Neural Networks,1993,4(1):73-85
[115]Zhang J,Walter GG.MiaoYubo,et al.Wavelet neural networks for function learning.IEEE Transactions on Signal Processing,1995,43(6):1485-1497
[116]Delyon B,Juditaky A,Benveniste A.Accuracy analysis for wavelet approximations.IEEE Transactions on Neural Networks,1995,6(2):332-348
[117]Li X L,Gao X P.Multiwavelet neural network:a novel model.2003 IEEE International Conference on Systems,Man and Cybernetics,Crystal City Hyatt Regency Washington D.C,USA,2003,3:2629-2632
[118]Gao X P,Xiao F.Multiwavelet networks for prediction of chaotic time series.2004 IEEE International Conference on Systems,Man and Cybernetics,2004,4:3328-3332
[119]Liu Z G.Multiwavelet neural networks construction study.2005 International Symposium on Signal,Circuits and Systems,2005,2:789-792
[120]Starzyk J A,EI-Gamal M A.Artificial neural network for testing analog circuits.IEEE International Symposium on Circuits and Systems,1990,3:1851-1854.
[121]He Y G,Deng Y,Sun Y C.Fault diagnosis of analog circuits with tolerances using artificial neural networks.Proceedings IEEE APCCAS,Tianjin,China,2000,292-295
[122]He Y G,Sun Y C.Neural network based Li-norm optimization approach for fault diagnosis of nonlinear circuits with tolerances.IEEE Circuits Device Systems,2001,148(2),223-228
[123]Catelani M,Fort A.Soft fault detection and isolation in analog circuits:some results and a comparison between a fuzzy approach and radial basis-function network.IEEE Transactions on Instrumentation and Measurement,2002,51(2):196-202
[124]Guo Q J,Yu H B,Xu A D.Wavelet fuzzy neural network for fault diagnosis.Proceedings 2005International Conference on Communications,Circuits and Systems,2005,2:27-30
[125]Tong D W,Walther E,Zalondek K C.Diagnosing an analog feedback system using model-based reasoning.Proceedings of the Annual AI Systems in Government Conference,1989,290-295
[126]Farmi A,Giua A,Sandoli E.Neural networks for multiple fault diagnosis in analog circuits.The IEEE International Workshop on Defect and Fault Tolerance in VLSI Systems,1993,303-310
[127]Ahmed S,Cheung P Y K.Analog fault diagnosis-A practical approach.1994 IEEE International Symposium on Circuits and Systems,1994,1:351-354
[128]Buntine W.A guide to the literature on learning probabilistic networks from data.IEEE Transactions on Knowledge and Data Engineering,1996,8(2):195-210
[129]Kirkland L V,Wright R G.Using neural networks to solve testing problems.IEEE Aerospace and Electronic Systens Magazine,1997,12(8):36-40
[130]Catelani M,Giraldi S.Fault diagnosis of analog circuits with model-based technique.1998Instrumentation and Measurement Technology Conference,1998,1:501-504
[131]Shojaei M,Sharif-Bakhtiar M.Automatic design of analog circuits based on behavioral model.IEEE Canadian Conference on Electrical and Computer Engineering,1998,1:145-148
[132]袁海英,陈光礻禹,谢永乐等.基于非线性电路频域核估计和神经网络的故障诊断.控制与决策,2007,22(4):473-476
[133]Ozawa T,Yamada M,Salama A.Diagnosability in the decomposition approach for fault location in large Analog circuits.IEEE Transactions on Circuits and Systems,1985,32(4):415-416
[134]Yuan H Y,Chen G J,Xie Y L.Feature extraction based on neural network in analog circuit fault diagnosis.Journal of Systems Engineering and Electronics,2007,18(2):434-437
[135]袁海英,陈光礻禹,谢永乐.故障诊断中基于神经网络的特征提取方法研究.仪器仪表学报,2007,28(1):90-94
[136]陈晓娟.模拟电路神经网络故障诊断方法研究:[博士学位论文].吉林:吉林大学,2006,35-37
[137]Martin T.Hagan,Howard B.Demuth,Mark H.Bcale.神经网络设计(戴葵,译者).北京:机械工业出版社,2002,232-235
[138]王承.基于神经网络的模拟电路故障诊断方法研究:[博士学位论文].成都:电子科技大学,2005,25
[139]毕天姝,倪以信,吴复立等.基于混合神经网络和遗传算法的故障诊断系统.现代电力,2005,22(1):31-36
[140]苗君明,佟刚,杨者青.基于遗传算法的神经网络优化.沈阳航空工业学院学报,2005,22(3):30-32
[141]梁化楼,戴贵亮.人工神经网络与遗传算法的结合进展及展望.电子学报,1995,23(10):194-200
[142]熊棱,赵明旺.基于遗传算法的BP网络全局收敛的混合智能学习算法.武汉科技大学学报(自然科学版),2000,23(2):183-186.
[143]史永胜,宋云霞.基于遗传算法与BP神经网络的故障诊断模型.计算机工程,2004,30(14):125-127
[144]田旭光,宋彤,刘宇新.结合遗传算法优化BP神经网络的结构和参数.计算机应用与软件,2004,21(6):69-71
[145]闫河,唐德东,黄扬帆等.基于遗传算法的BP网络快速自适应性算法.计算机仿真,2005,22(1):103-106
[146]金瑜,陈光礻禹,边海龙.基于移民算子遗传BP神经网络的模拟电路故障诊断.电子测量与仪器学报,2007,21(1):20-24
[147]陈伦军,罗延科,陈海红.机械优化设计遗传算法.北京:机械工业出版社,2005,50-52
[148]Kaminska B,Arabi K,Bell I,et al.Analog and mixed signal benchmark circuits first release.proceedings of International Test Conference,1997,183-190
[149]金瑜,陈光礻禹,刘红.基于BP小波神经网络的模拟电路故障诊断.测控技术,2007,26(7):64-66
[150]金瑜,陈光礻禹,刘红.基于小波神经网络的模拟电路故障诊断方法.仪器仪表学报,2007,28(9):1600-1604
[151]金瑜,陈光礻禹,刘红.基于非张量积小波网络的模拟电路故障诊断.仪器仪表学报,已录用
[152]Kovaǒevió J,Martin V.Nonseparable Multidimensional Perfect Reconstruction Filter Bank and Wavelet Bases for R~n.IEEE Transaction on Information Theory,1992,38(2):533-555
[153]高西奇,甘露,邹采荣.多小波变换的理论及其在图象处理中的应用.通信学报,1999,20(11):55-66
[154]程正兴,张玲玲.多小波分析及应用.工程数学学报,2001,18(1):99-107
[155]Jiao L C,Pan J,Fang Y W.Multiwavelet neural network and its approximation properties.IEEE Transactions on Neural Networks,2001,12(5):1060-1066
[156]金瑜,陈光礻禹,刘红.用多小波神经网络诊断模拟电路故障的方法.计算机辅助设计与图形学学报,2007,19(10):1247-1251
[157]金瑜,陈光礻禹,刘红.模拟电路故障诊断的新方法.仪器仪表学报,2007,28(10):1870-1873