基于支持向量机的模拟电子电路故障分类技术研究
|
摘要
随着微电子技术与半导体技术的快速发展,模拟电子系统的集成度越来越高,功能越来越复杂,而对其要求的可靠性却越来越高,但相应的可测性正变得越来越差。如何运用信号处理和人工智能技术测试和诊断模拟电子系统中的故障元件或子系统,是目前模拟诊断领域的一个热点。本文综合运用小波分解、特征选择等信号处理技术,并结合基于人工神经网络(ANN)、支持向量机分类器(SVC)等模式识别方法对待测模拟电子电路或系统的故障分类技术进行了研究。本文重点对以下几个方面进行了研究和探讨。
针对某些模拟电子系统中故障特征样本数量少且难以获取等问题,研究了一种基于云模型方法的模拟故障特征样本产生方法,并采用神经网络对新产生的扩展样本集进行训练。结果表明,利用新样本集训练过的神经网络对噪声干扰具有较好的鲁棒性。
针对模拟故障分类问题,研究了基于支持向量机的分类器设计方法,相关的创新点和贡献包括:提出了一种SVC平均计算量的分析指标,能够定性的对各种SVC的计算效率进行评估;对常规的one-against-rest SVC进行了改进,通过在决策阶段引入K近邻方法,实现了分类性能的提高;为提高one-against-rest SVC的测试效率,提出了一种基于故障字典和空间距离分析的混合故障分类器;提出了一种基于自组织特征映射神经网络(SOFM ANN)的聚类二叉树SVC的故障分类器,这种分类器可以根据样本的空间分布自适应的划分分类结构,从而能够减少测试误差,并给出了其平均计算量的计算方法。
为解决单一分类器在模拟电子测试中的不足,提出了一种基于分类器融合的模拟电子系统诊断方法,该方法采用神经网络和SVC作为子分类器,采用模糊推理技术对两种子分类器的输出进行决策融合,实验证明该项技术是有效的,并对噪声干扰有一定的鲁棒性。
故障特征提取和选择是模拟电子系统测试领域的关键技术,对于后续的故障分类十分重要。目前的相关研究侧重于故障特征提取,但对于特征选择的研究内容偏少。针对此问题,提出了一种基于标量小波系数的故障特征选择方法。该方法能够选择适合后续one-against-rest SVC分类器的特征,同时能够确定后续分类器的核参数,因此这项技术解决了SVC设计中的核参选择问题;为加快特征选择的速度和效率,提出了一种简化的混合one-against-rest SVC,这项技术能够提高特征选择效率,用于小波特征选择中取得了很好的效果。
本文还对实验方法进行了研究,制作了两类实验系统,分别基于PC机和数字信号控制器(DSC)。针对基于PC机的实验系统,分别设计了基于采集卡和数字信号处理器(DSP)的两种数据采集系统;基于DSC的实验系统可脱离PC机运行,利用软件实现了全部诊断任务,包括高速数据采集、基于信号处理的特征提取技术、基于改进型SVC的故障分类等。几个典型的实际模拟电路测试结果均较好的验证了所提出的方法的有效性和实用性。
With the development of microelectronics and semiconductor technologies, the integration of the analog electronic circuit system presents with high density and complicated functions. A higher reliability for such a system is required, but the system under consideration is always with low testability. Signal processing and artificial intelligence can be combined together to implement the testing and diagnosis of the analog circuit system at the level of components or sub-system. Such a task is also an interesting and hot subject in the domain of analog circuit testing and fault diagnosis.
This paper conducts the research of faults classification for analog electronic circuit under test (CUT) or system under test (SUT), using the technologies of signal processing, i.e. wavelet transformation and feature selection, and pattern classification, i.e. artificial neural network (ANN) and support vector machines classifier (SVC). In our study, several important parts are listed as follows. In some cases of analog circuit fault diagnosis, it is hard to obtain a large number of fault feature samples. Focusing on this problem, we present a novel method of extended set generation for fault feature samples based on cloud model. The artificial neural networks are used to train the extended sets. The experimental results show that the neural classifiers trained with these extended sets are robust to the random noise.
Focusing on the problem of fault classifier design, we conduct the research of fault classifier design based on support vector machines classifier (SVC) and, the diagnosis performances of these classifiers are also investigated. In this section, the contributions and the inventions are as follows. In order to evaluate the computational complexity of the SVC qualitatively, we propose a specification of average test calculations. We improve the conventional one-against-rest SVC, and the KNN classifier is introduced in the stage of fault decision. Such a design can improve the performance of the SVC. In order to reduce the testing time of the conventional one-against-rest SVC and improve the testing efficiency, a hybrid classifier based on the fault dictionary (FD) and space distance discriminant is proposed. We also invent a novel SVC, whose training structure is from the training of the self organization feature mapping ANN (SOFM ANN), and this classifier can adaptively find a good training structure according to the distributions of the training samples. This classifier can reduce the diagnosis errors and testing time, and in this part, we also demonstrate the calculation method of average test calculations for this classifier.
A single classifier has its drawbacks in diagnosing the analog electronic circuits and in our study, we present a fusion method at the level of decision classifiers. Two classifiers, the ANN and the SVC, are regarded as the sub-classifiers of the system. The fuzzy inference measure is adopted to realize the final decision. The experiments validate the effectiveness of the proposed method and this method is also robust to the random noise.
Both of fault feature extraction and selection are crucial to the faults classification of analog electronic system. At present, the feature extraction technique has received many attentions, but the feature selection technique has not been exploited with further research. Focusing on this problem, this paper proposes a selection technique of scalar wavelet features. This novel method can select the proper features for the subsequent one-against-rest SVC, and at the same time, the kernel function parameter is also determined. This technique solves the problem of kernel parameter selection without exhaustive searching, thus, a lot of calculations are saved. In order to speed up the feature selection process, a fast classifier based on the conventional one-against-rest SVC is proposed and this simple classifier can reduce the selection time significantly while good performance can be achieved.
In addition to these contents, we also conduct some real experiments. Two systems, based on the personal computer (PC) and the digital signal controller (DSC), are designed, respectively. For the first experimental system, the data collection system is realized with the data acquisition card (DAC) and the Digital Signal Processor (DSP), respectively. For the second system in independence of PC, the software is designed to implement all tasks, such as high-speed data collection, feature extraction based on advanced signal processing and fault decision based on improved SVC. Several real circuits are demonstrated to vindicate the effectiveness and validness of our proposed methods.
针对某些模拟电子系统中故障特征样本数量少且难以获取等问题,研究了一种基于云模型方法的模拟故障特征样本产生方法,并采用神经网络对新产生的扩展样本集进行训练。结果表明,利用新样本集训练过的神经网络对噪声干扰具有较好的鲁棒性。
针对模拟故障分类问题,研究了基于支持向量机的分类器设计方法,相关的创新点和贡献包括:提出了一种SVC平均计算量的分析指标,能够定性的对各种SVC的计算效率进行评估;对常规的one-against-rest SVC进行了改进,通过在决策阶段引入K近邻方法,实现了分类性能的提高;为提高one-against-rest SVC的测试效率,提出了一种基于故障字典和空间距离分析的混合故障分类器;提出了一种基于自组织特征映射神经网络(SOFM ANN)的聚类二叉树SVC的故障分类器,这种分类器可以根据样本的空间分布自适应的划分分类结构,从而能够减少测试误差,并给出了其平均计算量的计算方法。
为解决单一分类器在模拟电子测试中的不足,提出了一种基于分类器融合的模拟电子系统诊断方法,该方法采用神经网络和SVC作为子分类器,采用模糊推理技术对两种子分类器的输出进行决策融合,实验证明该项技术是有效的,并对噪声干扰有一定的鲁棒性。
故障特征提取和选择是模拟电子系统测试领域的关键技术,对于后续的故障分类十分重要。目前的相关研究侧重于故障特征提取,但对于特征选择的研究内容偏少。针对此问题,提出了一种基于标量小波系数的故障特征选择方法。该方法能够选择适合后续one-against-rest SVC分类器的特征,同时能够确定后续分类器的核参数,因此这项技术解决了SVC设计中的核参选择问题;为加快特征选择的速度和效率,提出了一种简化的混合one-against-rest SVC,这项技术能够提高特征选择效率,用于小波特征选择中取得了很好的效果。
本文还对实验方法进行了研究,制作了两类实验系统,分别基于PC机和数字信号控制器(DSC)。针对基于PC机的实验系统,分别设计了基于采集卡和数字信号处理器(DSP)的两种数据采集系统;基于DSC的实验系统可脱离PC机运行,利用软件实现了全部诊断任务,包括高速数据采集、基于信号处理的特征提取技术、基于改进型SVC的故障分类等。几个典型的实际模拟电路测试结果均较好的验证了所提出的方法的有效性和实用性。
With the development of microelectronics and semiconductor technologies, the integration of the analog electronic circuit system presents with high density and complicated functions. A higher reliability for such a system is required, but the system under consideration is always with low testability. Signal processing and artificial intelligence can be combined together to implement the testing and diagnosis of the analog circuit system at the level of components or sub-system. Such a task is also an interesting and hot subject in the domain of analog circuit testing and fault diagnosis.
This paper conducts the research of faults classification for analog electronic circuit under test (CUT) or system under test (SUT), using the technologies of signal processing, i.e. wavelet transformation and feature selection, and pattern classification, i.e. artificial neural network (ANN) and support vector machines classifier (SVC). In our study, several important parts are listed as follows. In some cases of analog circuit fault diagnosis, it is hard to obtain a large number of fault feature samples. Focusing on this problem, we present a novel method of extended set generation for fault feature samples based on cloud model. The artificial neural networks are used to train the extended sets. The experimental results show that the neural classifiers trained with these extended sets are robust to the random noise.
Focusing on the problem of fault classifier design, we conduct the research of fault classifier design based on support vector machines classifier (SVC) and, the diagnosis performances of these classifiers are also investigated. In this section, the contributions and the inventions are as follows. In order to evaluate the computational complexity of the SVC qualitatively, we propose a specification of average test calculations. We improve the conventional one-against-rest SVC, and the KNN classifier is introduced in the stage of fault decision. Such a design can improve the performance of the SVC. In order to reduce the testing time of the conventional one-against-rest SVC and improve the testing efficiency, a hybrid classifier based on the fault dictionary (FD) and space distance discriminant is proposed. We also invent a novel SVC, whose training structure is from the training of the self organization feature mapping ANN (SOFM ANN), and this classifier can adaptively find a good training structure according to the distributions of the training samples. This classifier can reduce the diagnosis errors and testing time, and in this part, we also demonstrate the calculation method of average test calculations for this classifier.
A single classifier has its drawbacks in diagnosing the analog electronic circuits and in our study, we present a fusion method at the level of decision classifiers. Two classifiers, the ANN and the SVC, are regarded as the sub-classifiers of the system. The fuzzy inference measure is adopted to realize the final decision. The experiments validate the effectiveness of the proposed method and this method is also robust to the random noise.
Both of fault feature extraction and selection are crucial to the faults classification of analog electronic system. At present, the feature extraction technique has received many attentions, but the feature selection technique has not been exploited with further research. Focusing on this problem, this paper proposes a selection technique of scalar wavelet features. This novel method can select the proper features for the subsequent one-against-rest SVC, and at the same time, the kernel function parameter is also determined. This technique solves the problem of kernel parameter selection without exhaustive searching, thus, a lot of calculations are saved. In order to speed up the feature selection process, a fast classifier based on the conventional one-against-rest SVC is proposed and this simple classifier can reduce the selection time significantly while good performance can be achieved.
In addition to these contents, we also conduct some real experiments. Two systems, based on the personal computer (PC) and the digital signal controller (DSC), are designed, respectively. For the first experimental system, the data collection system is realized with the data acquisition card (DAC) and the Digital Signal Processor (DSP), respectively. For the second system in independence of PC, the software is designed to implement all tasks, such as high-speed data collection, feature extraction based on advanced signal processing and fault decision based on improved SVC. Several real circuits are demonstrated to vindicate the effectiveness and validness of our proposed methods.
引文
[1] R. S. Berkowiz. Condition for network-element-value solvability[J]. IRE Transactions on circuits theory, 1962, 15(9): 25-29
[2] J. W. Bandler, A. E. Salama. Fault diagnosis of analog circuits[J]. Proceedings of IEEE, 1985, 73(8): 1279-1327
[3] Dong Liu. Analog and mixed-signal test and fault diagnosis[PhD dissertation]. USA: College of Engineering and Technology, Ohio University, 2003
[4] Douglas W. Brown, Patrick W. Kalgren, Carl S. Byington, et al. Electronic prognostics-a case study using global positioning system[J]. Microelectronics Reliability, 2007, 47: 1874-1881
[5]许丽佳.电子系统的故障预测与健康管理技术研究[博士学位论文].四川:电子科技大学, 2009
[6] Nikhil M. Vichare, Michael G. Pecht. Prognostics and health management of electronics[J]. IEEE Transactions on Components and Packaging Technologies, 2006, 29(1): 222-229
[7]曾声奎, Michael G. Pecht,吴际.故障预测与健康管理(PHM)技术的现状与发展[J].航空学报, 2005, 26(5): 626-632
[8]孙博,康锐,谢劲松.故障预测与健康管理系统研究和应用现状综述[J].系统工程与电子技术, 2007, 29(10): 1762-1767
[9] B. Kaminska, K. Arabi, I. Bell, et al. Analog and mixed-signal benchmark circuits-first release[C]. IEEE International Test Conference, Washington DC, 1997:183-190
[10] R. Kondagunturi, E. Bradley, K. Maggard, et al. Benchmark circuits for analog and mixed-signal testing[C]. Proceedings of IEEE Southeast Regional Conference, Lexington, KY, 1999:217-220
[11] J. A. Starzyk, J. Pang, S. Manetti, et al. Finding ambiguity groups in low testability analog circuits[J]. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, 2000, 47(8): 1125-1135
[12]杨士元.模拟电路故障诊断与可靠性设计[M].北京:清华大学出版社, 1993.12
[13] Wang Peng, Yang Shiyuan. A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math[J]. IEEE Transactions on Circuits and Systems-I: Regular Papers, 2005, 52(10): 2118-2127
[14] W. Hochwald, J. D. Bastian. A DC approach for analog fault dictionary determination[J]. IEEE Transactions on Circuits and Systems, 1979, 26(7): 523-529
[15] D. Grzechca, T. Golonek, J. Rutkowski. Analog fault AC dictionary creation-the fuzzy set approach[C]. IEEE International Symposium on Circuits and Systems(ISCAS), Island of Kos, Greece, 2006: 5744-5747
[16]陈圣俭,洪炳容,王月芳,等.可诊断容差模拟电路软故障的新故障字典法[J].电子学报, 2002, 28(2): 127-129
[17]胡梅,王红,杨士元.基于测前迭代仿真的模拟电路故障诊断方法[J].计算机辅助设计与图形学学报, 2010, 22(6): 914-920
[18]汪鹏,杨士元.电压增量的线性相关性及在电路测试中的应用[J].清华大学学报, 2007, 47(7): 1245-1248
[19]彭敏放,何怡刚.容差模拟电路的模糊软故障字典法诊断[J].湖南大学学报, 2005, 32(1): 25-28
[20]彭敏放,何怡刚.一种新的容差模拟电路故障屏蔽字典法[J].微电子学与计算机, 2004, 21(6): 199-202
[21]叶笠,王厚军,田书林.一种容差模拟电路故障诊断的新方法[J].电子测量与仪器学报, 2009, 23(7): 6-11
[22] C. Alippi, M. Catelani, A. Fort, et al. Automated selection of test frequencies for fault diagnosis in analog electronic circuits[J]. IEEE Transactions on Instrumentation and Measurement, 2005, 54(3): 1033-1043
[23] V. C. Prasad, S. N. R. Pinjala. Boolean method for selection of minimal set of test nodes for analogue fault dictionary[J]. Electronics Letters, 1993, 29(9): 747-74
[24] J. A. Starzyk, D. Liu, Z. Liu, et al. Entropy-based optimum test points selection for analog fault dictionary techniques[J]. IEEE Transactions on Instrumentation and Measurment, 2004, 53(3): 754-761
[25] T. Golonek, J. Rutkowski. Genetic-algorithm-based method for optimal analog test points selection[J]. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2007, 54(2): 117-121
[26]蔡金锭,马西奎,黄东泉.容差网络可及点优化方法的研究[J].电路与系统学报, 2000, 5(2): 23-27
[27] R. Spina, S. Upadhyaya. Linear circuit fault diagnosis using neuromorphic analyzers[J]. IEEE Transactions on Circuits And Systems-II: Analog and Digital Signal Processing, 1997, 44(3): 188-196
[28] M. Catelani, M. Gori. On the application of neural networks to fault diagnosis of electronic analog circuit[J]. Measurement, 1996, 17(2): 73-80
[29]何怡刚,罗先觉,邱关源.模拟电路故障诊断神经网络方法[J].湖南大学学报, 1996, 23(5): 90-94
[30]朱大奇,桑庆兵.光电雷达电子部件的量子神经网络故障诊断算法[J].电子学报,2006, 3(3): 573-576
[31] V. Litovski, M. Andrejevic, M. Zwolinski. Analogue electronic circuit diagnosis based on ANNs[J]. Microelectronics Reliability, 2006, 46(8): 1382-1391
[32] B. Cannas, A. Fanni, S. Manetti, et al. Neural network-based analog fault diagnosis using testability analysis[J]. Neural Computing & Applications, 2004, 13(4): 288-298
[33] D. Grzecha, J. Rutkowski. New concept analog fault diagnosis by creating two fuzzy-neural dictionaries test[C]. Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference(MELECON), Dubrovnic, Croatia, 2004, 1: 115-118
[34] S. S. Somayajula, E. Sánchez-Sinencio, J. P. Gyvez. Analog fault diagnosis: a fault clustering approach[C]. Proceedings of European Test Conference(ETC), 1993: 108-115
[35]唐志军,何怡刚,詹杰,等.基于符号分析的模拟电路故障诊断方法及实现[J].电工技术学报, 2008, 23(1): 24-29, 64
[36] F. Grasso, A. Luchetta, S. Manetti, et al. Symbolic techniques for the selection of test frequencies in analog fault diagnosis[J]. Analog Integrated Circuits and Signal Processing, 2004, 40(3): 205-213
[37]孙永奎.基于支持向量机的模拟电路故障诊断方法研究[博士学位论文].四川:电子科技大学, 2009
[38]毛先柏.基于支持向量机的模拟电路故障诊断研究[博士学位论文].湖北:华中科技大学, 2009
[39] R. Salat, S. Osowski. Analog filter diagnosis using support vector machine[C]. European Conference on Circuit Theory and Design (ECCTD), Krakow, 2003: 421-424
[40]万九卿,李行善.基于串行支持向量分类器的模拟电路故障诊断[J].北京航空航天大学学报, 2003, 29(9): 789-792
[41] K. Siwek, S. Osowski, T. Markiewicz. Support vector machine for fault diagnosis in electrical circuits[C]. Proceedings of the 7th Nordic Signal Processing Symposium(NORSIG), Reykjavik, Iceland, 2006: 342-345
[42] D. Grzechca, S. Czeczótka. Faults classification in analog electronic circuits with use of the SVM algorithm[C]. European Conference on Circuit Theory and Design (ECCTD), Antalya, 2009: 659-662
[43]连可,王厚军,龙兵.基于SVM的模拟电子系统多故障诊断研究[J].仪器仪表学报, 2007, 28(6): 1029-1034
[44]左磊,侯立刚,张旺,等.基于粒子群-支持向量机的模拟电路故障诊断[J].系统工程与电子技术, 2010, 32(7): 1553-1556
[45]王安娜,邱增,吴洁,等.基于SVM多类分类算法的模拟电路软故障诊断[J].东北大学学报(自然科学版), 2008, 29(7): 924-927
[46]黄亮,侯建军,刘颖,等.基于相量分析与支持向量机的交流电路故障诊断[J].北京交通大学学报, 2008, 32(5): 30-33
[47]崔江,王友仁.基于聚类预处理和支持向量机的模拟电路故障诊断技术[J].计算机应用, 2006, 26(8): 1977-1979
[48] J. Cui, Y. Wang. A novel approach of analog circuit fault diagnosis using support vector machines classifier[J]. Measurement, 2011, 44(1): 281-289
[49] D. Grzechca, J. Rutkowski. Fault diagnosis in analog electronic circuits-the SVM approach[J]. Metrology and Measurement Systems, 2009, 16(4): 583-597
[50] M. A. El-Gamal, M. Abdulghafour. Fault isolation in analog circuits using a fuzzy inference system [J]. Computers and Electrical Engineering, 2003, 29(1): 213-229
[51]周龙甫,师奕兵,唐静远,等.基于模糊线性规划的模拟电路软故障诊断[J].仪器仪表学报, 2009, 30(5): 899-904
[52] Z. Liu, F. Yan. Combing a neural network with case-based resoning in a diagnostic system[J]. Artificial intelligence in medicine, 1997, 9(1): 5-27
[53] C. Pous, J. Colomer, J. Melendez. Extending a fault dictionary towards a Case Based Reasoning system for linear electronic analog circuits diagnosis[C]. Proceedings of the 7th European Conference Advances in Case-Based Reasoning(ECCBR), Spain, Madrid, 2004: 748-762
[54] M. A. EL-GAMAL, M. F. ABUEL-YAZEED. A combined clustering and neural network approach for analog multiple hard fault classification[J]. Journal of Electronic Testing: Theory and Applications, 1999, 14(3): 207-217
[55] Y. Tan, Y. He. A novel method for fault diagnosis of analog circuits based on WP and GPNN[J]. International Journal of Electronics, 2008, 95(5): 431-439
[56]刘美容.基于遗传算法、小波与神经网络的模拟电路故障诊断方法[博士学位论文].湖南:湖南大学, 2009
[57]祝文姬,何怡刚.基于遗传BP网络的模拟电路故障诊断方法及其应用[J].计算机辅助设计与图形学学报, 2009, 21(9): 1283-1289
[58] O. Chapelle, P. Haffner, V. N. Vapnik. Support vector machines for histogram-based image classification[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 1055-1064
[59]彭敏放,何怡刚.容差电路的K故障屏蔽方法研究[J].电路与系统学报, 2002, 7(2): 92-96
[60]何怡刚,徐卫林,厉芸.基于神经网络和广义互测试的大规模电路故障诊断[J].湖南大学学报(自然科学版), 2004, 31(6): 38-40, 59
[61] M. Tadeusiewicz, S. Ha?gas. An algorithm for multiple fault diagnosis in analogue circuits[J]. International Journal of Circuit Theory and Applications, 2006, 34(6): 607-615
[62]彭敏放.容差模拟电路故障诊断屏蔽理论与信息融合方法研究[湖南大学博士学位论文].湖南:湖南大学, 2006
[63]罗慧,王友仁,崔江,等.电力电子电路多源特征层融合故障诊断方法[J].电机与控制学报, 2010, 14(4): 92-96
[64]杨易旻,彭敏放,王嘉家,等.考虑异质信息不相容的模拟电路融合诊断[J].仪器仪表学报, 2009, 30(11): 2322-2329
[65] Y. Dai, J. Xu. Analog circuit fault diagnosis based on noise measurement[J]. Microelectronics Reliability, 1999, 39(8): 1293-1298
[66] J. Wan, X. Li. PCB infrared thermal imaging diagnosis using zvector classifier[C]. IEEE Proceedings of the 4th World Congress on Intelligent Control and Automation(WCICA), Shanghai, China, 2002: 2718-2722
[67] F. Aminian, M. Aminian. Fault diagnosis of nonlinear analog circuits using neural networks with wavelet and fourier transforms as preprocessors [J]. Journal of Electronic Testing: Theory and Applications, 2001, 17(6): 471-481
[68] M. A. EL-GAMAL, M. D. A. MOHAMED. Ensembles of neural networks for fault diagnosis in analog circuits[J]. Journal of Electronic Testing: Theory and Applications, 2007, 23(4): 323-339
[69] L. Oukhellou, A. Debiolles, T. Denoeux, et al. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion[J]. Engineering Applications of Artificial Intelligence, 2010, 23(1): 117-128
[70]彭敏放,何怡刚,王耀南,等.模拟电路的融合智能故障诊断[J].中国电机工程学报, 2006.2, 26(3): 19-24
[71]唐静远,师奕兵,张伟.基于支持向量机集成的模拟电路故障诊断[J].仪器仪表学报,2008.06, 29(6): 1216-1220
[72]刘冲,张均东,曾鸿,等.基于支持向量机的无穷维AdaBoost算法及其应用[J].仪器仪表学报, 2010, 31(4): 764-769
[73]朱大奇,杨永清,于盛林.电子部件故障诊断的Dempster-Shafer信息融合算法[J].控制理论与应用, 2004, 21(4): 659-663
[74]彭羽中,樊锐,刘强.基于人工神经网络的电路故障诊断专家系统[J].系统工程与电子技术, 2002, 24(10): 116-119
[75]刘刚,张洪刚,郭军.不同训练样本对识别系统的影响[J].计算机学报, 2005, 28(11): 1923-1928
[76]杨慧中,卢鹏飞,张素贞,等.网络泛化能力与随机扩展训练集[J].控制理论与应用, 2002, 19(6): 963-966
[77] N. K. George. On over fitting, generalization, and randomly expanded training sets[J]. IEEE Transactions on Neural Networks, 2000, 11(5): 1050-1057
[78]解培中,张志涌.一种新的用于故障诊断分类器的特征样本生成方法[J].系统工程与电子技术, 2001, 23(11): 35-37
[79]李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社, 2005
[80]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[J].电路与系统学报, 2004, 4(2): 54-57
[81] M. Catelani, A. Fort. Fault diagnosis of electronic analog circuits using a radial basis function network classifier[J]. Measurement, 2000, 28(3): 147-158
[82] M. Catelani, A. Fort. Soft fault detection and isolation in analog circuits: some results and a comparison between a fuzzy approach and radial basis function networks[J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51(2): 196-202
[83]韩晓静,王友仁,崔江.基于小波径向基网络的电力电子电路故障诊断[J].微电子学, 2008, 38(3): 309-311
[84]李晴,何怡刚,包伟.免疫蚂蚁算法优化的RBF网络用于模拟电路故障诊断[J].仪器仪表学报, 2010, 31(6): 1255-1261
[85] F. Aminian, M. Aminian. Fault diagnosis of analog circuits using bayesian neural networks with wavelet transform as preprocessor[J]. Journal of Electronic Testing: Theory and Applications, 2001, 17(1): 29-36
[86]谢宏.基于Volterra级数、小波分析与神经网络的非线性网络故障诊断方法的研究[博士学位论文].湖南:湖南大学, 2005
[87] El-Gamal, Mohamed A.. Fault location and parameter identification in analog circuits[phD dissertation]. USA: Ohio University (the college of Engineering and Technology), 1990
[88]张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000, 26(1): 32-42
[89]边肇祺,张学工,等.模式识别[M].北京:清华大学出版社, 2000
[90] C. Cortes, V.N. Vapnik. Support vector networks[J]. Machine Learning, 1995, 20(3): 273-297
[91] C. J.C. Burges. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167
[92] V. N. Vapnik. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988-999
[93] C. W. Hsu, C. J. Lin. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425
[94] D. Anguita, S. Ridella, D. Sterpi. A new method for multi-class support vector machines[C]. Proceedings of IEEE International Joint Conference on Neural Networks(IJCNN), Budapest, Hungary, 2004, 1: 407-412
[95] Y. Zhang, X. Wei, H. Jiang. One-class classifier based on SBT for analog circuit faultdiagnosis[J]. Measurement, 2008, 41(4): 371-380
[96]何学文.基于支持向量机的故障智能诊断理论与方法研究[博士学位论文].湖南:中南大学, 2004
[97]张永.基于模糊支持向量机的多类分类算法研究[博士学位论文].辽宁:大连理工大学, 2008
[98] J. A. K. Suykens, J. Vandewalle. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300
[99] B. Scho1kopf, J. C. Platt, J. C. Shawe-Taylor, et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7): 1443-1471
[100] H. G. Chew, R. E. Bogner, C. C. Lim. Dualν-support vector machine with error rate and training size biasing[C]. In proceedings of the 26th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Salt Lake City, 2001, (2): 1269-1272
[101] http://www.support-vector-machines.org/SVM_soft.html
[102] http://www.princeton.edu/~kung/ele571/571-MatLab/571svm/
[103]胡清,王荣杰,詹宜巨.基于支持向量机的电力电子电路故障诊断技术[J].中国电机工程学报, 2008, 28(12): 107-111
[104]李蓉,叶世伟,史忠植. SVM-KNN分类器-一种提高SVM分类精度的新方法[J].电子学报, 2002, 30(5): 745-748
[105]鲁昌华,汪涌,王玲飞.基于PCA和SVM的模拟电路故障诊断[J].电子测量与仪器学报, 2008, 22(3): 64-68
[106] F. Schwenker. Hierarchical support vector machines for multi-class pattern recognition[C]. Proceedings of the fourth international conference on knowledge-based intelligent engineering systems & allied technologies, 2000, Brighton, UK, 2: 561-565
[107]孙永奎,陈光礻禹,李辉.模糊聚类与SVM诊断模拟电路单软故障的方法[J].计算机辅助设计与图形学学报, 2008, 20(5): 612-617
[108] F. Takahashi, S. Abe. Decision-tree-based multi-class support vector machines[C]. Proceedings of the 9th International Conference on Neural Information Processing(ICNP), Singapore, 2002: 1418-1422, Singapore
[109]张智星,孙春在,水谷英二.神经-模糊和软计算[M].西安:西安交通大学出版社, 2000
[110] A. Wang, J. Liu, H. Li, et al. A novel algorithm for fault diagnosis of analog circuit with tolerances using improved binary-tree svms based on somnn clustering[C]. Third International Conference on Natural Computation (ICNC), Hai Kou, Hainan, China, 2007, 1: 491-496
[111]连可,黄建国,王厚军,等.一种基于遗传算法的SVM决策树多分类策略研究[J].电子学报, 2008, 36(8): 1502-1507
[112] F. Aminian, M. Aminian, H. W. Collins. Analog fault diagnosis of actual circuits using neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51(3): 544-549
[113] Yigang He, Yanghong Tan, Yichuang Sun. Fault diagnosis of analog circuits based on wavelet packets[C]. IEEE Region 10 Conference(TENCON), Thailand, 2004: 267-270
[114]谭阳红,叶佳卓.模拟电路故障诊断的小波方法[J].电子与信息学报,2006, 28(9): 1748-1751
[115]王成华,王友仁,胡志忠.现代电子技术基础(模拟部分)[M].北京:北京航空航天大学出版社, 2005
[116] M. Aminian, F. Aminian. A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor[J]. IEEE Transactions on Instrumentation and Measurement, 2007, 56(5): 1546-1554
[117] Y. He, Y. Tan, Y. Sun. Wavelet neural network approach for fault diagnosis of analogue circuits[J]. IEE Proceedings of Circuits Devices Systems, 2004, 151(4): 379-384
[118]翟永杰.基于支持向量机的故障智能诊断方法研究[博士学位论文].河北:华北电力大学, 2004
[119]易辉,宋晓峰,姜斌,等.基于结点优化的决策导向无环图支持向量机及其在故障诊断中的应用[J].自动化学报, 2010, 36(3): 427-432
[120] M. K. IYER, M. L. BUSHNELL. Effect of noise on analog circuit testing[J]. Journal of Electronic Testing: Theory and Applications, 1999, 15(1): 11-22
[121] S. Bhunia, K. Roy. Dynamic supply current testing of analog circuits using wavelet transform[C]. Proceedings of the 20th IEEE VLSI Test Symposium(VTS), Montery, CA, 2002: 302-308
[122]钱志鸿,张琳.用噪声测量方法分析模拟电路故障的研究[J].仪器仪表学报, 2005, 26(6): 650-657
[123]张超杰,贺国,梁述海.小波变换与主元分析相结合的模拟电路检测方法[J].哈尔滨工程大学学报, 2010, 31(5): 570-575
[124]程军圣,于德介,杨宇.基于内廪模态奇异值分解和支持向量机的故障诊断方法[J].自动化学报, 2006, 32(3): 475-480
[125]徐晨曦,陈光礻禹.基于响应误差矩阵SVD的软故障诊断方法[J].仪器仪表学报, 2006, 27(9): 1100-1103
[126]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[J].控制与决策, 2006, 21(10): 1134-1137, 1142
[127]崔江,王友仁,刘权.基于高阶谱与支持向量机的电力电子电路故障诊断技术[J].中国电机工程学报, 2007, 27(10): 62-66
[128]侯青剑,王宏力.一种基于EMD的模拟电路故障特征提取方法[J].系统工程与电子技术, 2009, 31(6): 1525-528
[129]唐静远,师奕兵,周龙甫,等.非线性模拟电路故障诊断的小波领袖多重分形分析方法[J].控制与决策, 2010, 25(4): 605-609
[130] L. Yuan, Y. He, J. Huang, et al. A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(3): 586-595
[131]罗慧,王友仁,崔江.基于最优分数阶傅里叶变换的模拟电路故障特征提取新方法[J].仪器仪表学报, 2009, 30(5): 997-1001
[132] M. Aminian, F. Aminian. Neural-network based analog-circuit diagnosis using wavelet transform as preprocessor[J]. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, 2000, 47(2):151-156
[133]王军锋,张维强,宋国乡.模拟电路故障诊断的多小波神经网络算法[J].电工技术学报, 2006, 21(1): 33-36
[134]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[J].计算机辅助设计与图形学学报, 2007, 19(10): 1247-1251
[135]金瑜,陈光礻禹,刘红.模拟电路故障诊断的新方法[J].仪器仪表学报, 2007, 28(10): 1870-1873
[136]于飞,田玲玲,刘喜梅,等.基于小波多分辨率分析和主元分析的电力电子电路故障诊断[J].华东理工大学学报,2006,32(9): 1113-1116,1125
[137]王承,陈光礻禹,谢永乐.基于动态电源电流测试的模拟电路故障诊断[J].系统工程与电子技术, 2005, 27(3): 413-416
[138] P. Kalpana, K. Gunavathi. Wavelet based fault detection in analog VLSI circuits using neural networks[J]. Applied Soft Computing, 2008, 8: 1592-1598
[139] V. Stopjaková, P. Malosek, M. Matej, et al. Defect detection in analog and mixed circuits by neural networks using wavelet analysis[J]. IEEE Transactions on Reliability, 2005, 54(3): 441-448
[140] M. G. Dimopoulos, A. D. Spyronasios, D. K. Papakostas, et al. Wavelet energy-based testing using supply current measurements[J]. IET Science, Measurement and Technology, 2009, 4(2): 76-85
[141]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[J].仪器仪表学报, 2007, 28(1): 90-94
[142]唐静远,师奕兵,周龙甫,等.基于交叉熵方法和支持向量机的模拟电路故障诊断[J].控制与决策, 2009, 24(9): 1416-1420
[143]许丽佳,王厚军,龙兵.一种状态监测与健康评估方法及其在模拟电路中的应用[J].计算机辅助设计与图形学学报, 2008, 20(12): 1550-1556
[144] S. G. Mallat. A theory for multi-resolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
[145] R. Kohavi, G. H. Hohn. Wrappers for feature subset selection[J]. Artificial Intelligence, 1997, 97: 273-324
[146] Y. Liu, Y. Zheng. FS_SFS: A novel feature selection method for support vector machines[J]. Pattern Recognition, 2006, 39: 1333-1345
[147]崔宇红,倪国强,陈永飞.分布式多传感器的模糊决策融合[J].激光与红外, 2005, 35(1): 61-64
[148]尉询楷,李应红,刘建勋,等.基于支持向量机的信息融合诊断方法[J].系统工程与电子技术, 2005, 27(9): 1665-1668
[149] Peng Minfang, Shen Meie, He Yigang, et al. Analog circuit diagnosis using rbf network and D-S evidential reasoning[J]. Transactions of China Electrotechnical Society, 2009, 24(8): 6-12
[150]朱剑英.智能系统非经典数学方法[M].武汉:华中科技大学出版社, 2004
[151] Y. Maidon, B. W. Jervis, P. Fouillat, et al. Using artificial neural networks or Lagrange interpolation to characterize the faults in an analog circuit: an experimental study[J]. IEEE Transactions on Instrumentation and Measurement, 1999, 48(5): 932-938
[152] Z. Czaja. A diagnosis method of analog parts of mixed-signal systems controlled by microcontrollers[J]. Measurement, 2007, 40(2): 158-170
[153] A. M. Imam, T. G. Habetler, R. G. Harley, et al. Failure prediction of electrolytic capacitor using DSP methods[C]. Proceedings of the 20th IEEE Applied Power Electronics Conference(APEC), 2005, Austin, TX, 2: 965-970
[154] Texas Instrument. TMS320LF2407 DSP Controllers. 1999
[155]刘和平,严利平,张学锋,等. TMS320LF240x DSP结构、原理及应用[M].北京:北京航空航天大学出版社, 2002
[156] Maxim Integrated Products. Max125/Max126 data sheet. 2008
[157]毅刚,付平,王丽.采用数字相关法测量相位差[J].计量学报, 2000, 21(3): 216-221
[158]苏奎峰,吕强,邓志东,等. TMS320x28xxx原理与开发[M].北京:电子工业出版社, 2009
[159] Texas Instruments. TMS320F28335 Digital Signal Controller (DSC) data manual. 2007
[160] Texas Instruments. TMS320xF2833x Analog-to-Digital Converter (ADC) Module Reference Guide. 2007
[161] Texas Instruments. TMS320xF2833x DSC External Interface (XINTF) Reference Guide. 2007
[162]王珊珊,藏冽,张志航,等.程序设计语言-C[M].北京:清华大学出版社, 2007
[163] Z. Luo, Z. Shi. On electronic equipment fault diagnosis using least squares wavelet support vector machines[C]. Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA), Dalian, China, 2006: 6193-6197
[164] D. J. Sebald, J. A. Bucklew. Support vector machines and the multiple hypothesis test problem[J]. IEEE Transactions on Signal Processing, 2001, 49(11): 2865-2872
[165]杨智明.面向不平衡数据的支持向量机分类方法研究[博士学位论文].黑龙江:哈尔滨工业大学, 2009
[166]宋其江,徐敏强,王日新.基于分层有向图的航天器故障诊断[J].航空学报, 2009, 30(6): 1058-1062
[2] J. W. Bandler, A. E. Salama. Fault diagnosis of analog circuits[J]. Proceedings of IEEE, 1985, 73(8): 1279-1327
[3] Dong Liu. Analog and mixed-signal test and fault diagnosis[PhD dissertation]. USA: College of Engineering and Technology, Ohio University, 2003
[4] Douglas W. Brown, Patrick W. Kalgren, Carl S. Byington, et al. Electronic prognostics-a case study using global positioning system[J]. Microelectronics Reliability, 2007, 47: 1874-1881
[5]许丽佳.电子系统的故障预测与健康管理技术研究[博士学位论文].四川:电子科技大学, 2009
[6] Nikhil M. Vichare, Michael G. Pecht. Prognostics and health management of electronics[J]. IEEE Transactions on Components and Packaging Technologies, 2006, 29(1): 222-229
[7]曾声奎, Michael G. Pecht,吴际.故障预测与健康管理(PHM)技术的现状与发展[J].航空学报, 2005, 26(5): 626-632
[8]孙博,康锐,谢劲松.故障预测与健康管理系统研究和应用现状综述[J].系统工程与电子技术, 2007, 29(10): 1762-1767
[9] B. Kaminska, K. Arabi, I. Bell, et al. Analog and mixed-signal benchmark circuits-first release[C]. IEEE International Test Conference, Washington DC, 1997:183-190
[10] R. Kondagunturi, E. Bradley, K. Maggard, et al. Benchmark circuits for analog and mixed-signal testing[C]. Proceedings of IEEE Southeast Regional Conference, Lexington, KY, 1999:217-220
[11] J. A. Starzyk, J. Pang, S. Manetti, et al. Finding ambiguity groups in low testability analog circuits[J]. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, 2000, 47(8): 1125-1135
[12]杨士元.模拟电路故障诊断与可靠性设计[M].北京:清华大学出版社, 1993.12
[13] Wang Peng, Yang Shiyuan. A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math[J]. IEEE Transactions on Circuits and Systems-I: Regular Papers, 2005, 52(10): 2118-2127
[14] W. Hochwald, J. D. Bastian. A DC approach for analog fault dictionary determination[J]. IEEE Transactions on Circuits and Systems, 1979, 26(7): 523-529
[15] D. Grzechca, T. Golonek, J. Rutkowski. Analog fault AC dictionary creation-the fuzzy set approach[C]. IEEE International Symposium on Circuits and Systems(ISCAS), Island of Kos, Greece, 2006: 5744-5747
[16]陈圣俭,洪炳容,王月芳,等.可诊断容差模拟电路软故障的新故障字典法[J].电子学报, 2002, 28(2): 127-129
[17]胡梅,王红,杨士元.基于测前迭代仿真的模拟电路故障诊断方法[J].计算机辅助设计与图形学学报, 2010, 22(6): 914-920
[18]汪鹏,杨士元.电压增量的线性相关性及在电路测试中的应用[J].清华大学学报, 2007, 47(7): 1245-1248
[19]彭敏放,何怡刚.容差模拟电路的模糊软故障字典法诊断[J].湖南大学学报, 2005, 32(1): 25-28
[20]彭敏放,何怡刚.一种新的容差模拟电路故障屏蔽字典法[J].微电子学与计算机, 2004, 21(6): 199-202
[21]叶笠,王厚军,田书林.一种容差模拟电路故障诊断的新方法[J].电子测量与仪器学报, 2009, 23(7): 6-11
[22] C. Alippi, M. Catelani, A. Fort, et al. Automated selection of test frequencies for fault diagnosis in analog electronic circuits[J]. IEEE Transactions on Instrumentation and Measurement, 2005, 54(3): 1033-1043
[23] V. C. Prasad, S. N. R. Pinjala. Boolean method for selection of minimal set of test nodes for analogue fault dictionary[J]. Electronics Letters, 1993, 29(9): 747-74
[24] J. A. Starzyk, D. Liu, Z. Liu, et al. Entropy-based optimum test points selection for analog fault dictionary techniques[J]. IEEE Transactions on Instrumentation and Measurment, 2004, 53(3): 754-761
[25] T. Golonek, J. Rutkowski. Genetic-algorithm-based method for optimal analog test points selection[J]. IEEE Transactions on Circuits and Systems-II: Express Briefs, 2007, 54(2): 117-121
[26]蔡金锭,马西奎,黄东泉.容差网络可及点优化方法的研究[J].电路与系统学报, 2000, 5(2): 23-27
[27] R. Spina, S. Upadhyaya. Linear circuit fault diagnosis using neuromorphic analyzers[J]. IEEE Transactions on Circuits And Systems-II: Analog and Digital Signal Processing, 1997, 44(3): 188-196
[28] M. Catelani, M. Gori. On the application of neural networks to fault diagnosis of electronic analog circuit[J]. Measurement, 1996, 17(2): 73-80
[29]何怡刚,罗先觉,邱关源.模拟电路故障诊断神经网络方法[J].湖南大学学报, 1996, 23(5): 90-94
[30]朱大奇,桑庆兵.光电雷达电子部件的量子神经网络故障诊断算法[J].电子学报,2006, 3(3): 573-576
[31] V. Litovski, M. Andrejevic, M. Zwolinski. Analogue electronic circuit diagnosis based on ANNs[J]. Microelectronics Reliability, 2006, 46(8): 1382-1391
[32] B. Cannas, A. Fanni, S. Manetti, et al. Neural network-based analog fault diagnosis using testability analysis[J]. Neural Computing & Applications, 2004, 13(4): 288-298
[33] D. Grzecha, J. Rutkowski. New concept analog fault diagnosis by creating two fuzzy-neural dictionaries test[C]. Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference(MELECON), Dubrovnic, Croatia, 2004, 1: 115-118
[34] S. S. Somayajula, E. Sánchez-Sinencio, J. P. Gyvez. Analog fault diagnosis: a fault clustering approach[C]. Proceedings of European Test Conference(ETC), 1993: 108-115
[35]唐志军,何怡刚,詹杰,等.基于符号分析的模拟电路故障诊断方法及实现[J].电工技术学报, 2008, 23(1): 24-29, 64
[36] F. Grasso, A. Luchetta, S. Manetti, et al. Symbolic techniques for the selection of test frequencies in analog fault diagnosis[J]. Analog Integrated Circuits and Signal Processing, 2004, 40(3): 205-213
[37]孙永奎.基于支持向量机的模拟电路故障诊断方法研究[博士学位论文].四川:电子科技大学, 2009
[38]毛先柏.基于支持向量机的模拟电路故障诊断研究[博士学位论文].湖北:华中科技大学, 2009
[39] R. Salat, S. Osowski. Analog filter diagnosis using support vector machine[C]. European Conference on Circuit Theory and Design (ECCTD), Krakow, 2003: 421-424
[40]万九卿,李行善.基于串行支持向量分类器的模拟电路故障诊断[J].北京航空航天大学学报, 2003, 29(9): 789-792
[41] K. Siwek, S. Osowski, T. Markiewicz. Support vector machine for fault diagnosis in electrical circuits[C]. Proceedings of the 7th Nordic Signal Processing Symposium(NORSIG), Reykjavik, Iceland, 2006: 342-345
[42] D. Grzechca, S. Czeczótka. Faults classification in analog electronic circuits with use of the SVM algorithm[C]. European Conference on Circuit Theory and Design (ECCTD), Antalya, 2009: 659-662
[43]连可,王厚军,龙兵.基于SVM的模拟电子系统多故障诊断研究[J].仪器仪表学报, 2007, 28(6): 1029-1034
[44]左磊,侯立刚,张旺,等.基于粒子群-支持向量机的模拟电路故障诊断[J].系统工程与电子技术, 2010, 32(7): 1553-1556
[45]王安娜,邱增,吴洁,等.基于SVM多类分类算法的模拟电路软故障诊断[J].东北大学学报(自然科学版), 2008, 29(7): 924-927
[46]黄亮,侯建军,刘颖,等.基于相量分析与支持向量机的交流电路故障诊断[J].北京交通大学学报, 2008, 32(5): 30-33
[47]崔江,王友仁.基于聚类预处理和支持向量机的模拟电路故障诊断技术[J].计算机应用, 2006, 26(8): 1977-1979
[48] J. Cui, Y. Wang. A novel approach of analog circuit fault diagnosis using support vector machines classifier[J]. Measurement, 2011, 44(1): 281-289
[49] D. Grzechca, J. Rutkowski. Fault diagnosis in analog electronic circuits-the SVM approach[J]. Metrology and Measurement Systems, 2009, 16(4): 583-597
[50] M. A. El-Gamal, M. Abdulghafour. Fault isolation in analog circuits using a fuzzy inference system [J]. Computers and Electrical Engineering, 2003, 29(1): 213-229
[51]周龙甫,师奕兵,唐静远,等.基于模糊线性规划的模拟电路软故障诊断[J].仪器仪表学报, 2009, 30(5): 899-904
[52] Z. Liu, F. Yan. Combing a neural network with case-based resoning in a diagnostic system[J]. Artificial intelligence in medicine, 1997, 9(1): 5-27
[53] C. Pous, J. Colomer, J. Melendez. Extending a fault dictionary towards a Case Based Reasoning system for linear electronic analog circuits diagnosis[C]. Proceedings of the 7th European Conference Advances in Case-Based Reasoning(ECCBR), Spain, Madrid, 2004: 748-762
[54] M. A. EL-GAMAL, M. F. ABUEL-YAZEED. A combined clustering and neural network approach for analog multiple hard fault classification[J]. Journal of Electronic Testing: Theory and Applications, 1999, 14(3): 207-217
[55] Y. Tan, Y. He. A novel method for fault diagnosis of analog circuits based on WP and GPNN[J]. International Journal of Electronics, 2008, 95(5): 431-439
[56]刘美容.基于遗传算法、小波与神经网络的模拟电路故障诊断方法[博士学位论文].湖南:湖南大学, 2009
[57]祝文姬,何怡刚.基于遗传BP网络的模拟电路故障诊断方法及其应用[J].计算机辅助设计与图形学学报, 2009, 21(9): 1283-1289
[58] O. Chapelle, P. Haffner, V. N. Vapnik. Support vector machines for histogram-based image classification[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 1055-1064
[59]彭敏放,何怡刚.容差电路的K故障屏蔽方法研究[J].电路与系统学报, 2002, 7(2): 92-96
[60]何怡刚,徐卫林,厉芸.基于神经网络和广义互测试的大规模电路故障诊断[J].湖南大学学报(自然科学版), 2004, 31(6): 38-40, 59
[61] M. Tadeusiewicz, S. Ha?gas. An algorithm for multiple fault diagnosis in analogue circuits[J]. International Journal of Circuit Theory and Applications, 2006, 34(6): 607-615
[62]彭敏放.容差模拟电路故障诊断屏蔽理论与信息融合方法研究[湖南大学博士学位论文].湖南:湖南大学, 2006
[63]罗慧,王友仁,崔江,等.电力电子电路多源特征层融合故障诊断方法[J].电机与控制学报, 2010, 14(4): 92-96
[64]杨易旻,彭敏放,王嘉家,等.考虑异质信息不相容的模拟电路融合诊断[J].仪器仪表学报, 2009, 30(11): 2322-2329
[65] Y. Dai, J. Xu. Analog circuit fault diagnosis based on noise measurement[J]. Microelectronics Reliability, 1999, 39(8): 1293-1298
[66] J. Wan, X. Li. PCB infrared thermal imaging diagnosis using zvector classifier[C]. IEEE Proceedings of the 4th World Congress on Intelligent Control and Automation(WCICA), Shanghai, China, 2002: 2718-2722
[67] F. Aminian, M. Aminian. Fault diagnosis of nonlinear analog circuits using neural networks with wavelet and fourier transforms as preprocessors [J]. Journal of Electronic Testing: Theory and Applications, 2001, 17(6): 471-481
[68] M. A. EL-GAMAL, M. D. A. MOHAMED. Ensembles of neural networks for fault diagnosis in analog circuits[J]. Journal of Electronic Testing: Theory and Applications, 2007, 23(4): 323-339
[69] L. Oukhellou, A. Debiolles, T. Denoeux, et al. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion[J]. Engineering Applications of Artificial Intelligence, 2010, 23(1): 117-128
[70]彭敏放,何怡刚,王耀南,等.模拟电路的融合智能故障诊断[J].中国电机工程学报, 2006.2, 26(3): 19-24
[71]唐静远,师奕兵,张伟.基于支持向量机集成的模拟电路故障诊断[J].仪器仪表学报,2008.06, 29(6): 1216-1220
[72]刘冲,张均东,曾鸿,等.基于支持向量机的无穷维AdaBoost算法及其应用[J].仪器仪表学报, 2010, 31(4): 764-769
[73]朱大奇,杨永清,于盛林.电子部件故障诊断的Dempster-Shafer信息融合算法[J].控制理论与应用, 2004, 21(4): 659-663
[74]彭羽中,樊锐,刘强.基于人工神经网络的电路故障诊断专家系统[J].系统工程与电子技术, 2002, 24(10): 116-119
[75]刘刚,张洪刚,郭军.不同训练样本对识别系统的影响[J].计算机学报, 2005, 28(11): 1923-1928
[76]杨慧中,卢鹏飞,张素贞,等.网络泛化能力与随机扩展训练集[J].控制理论与应用, 2002, 19(6): 963-966
[77] N. K. George. On over fitting, generalization, and randomly expanded training sets[J]. IEEE Transactions on Neural Networks, 2000, 11(5): 1050-1057
[78]解培中,张志涌.一种新的用于故障诊断分类器的特征样本生成方法[J].系统工程与电子技术, 2001, 23(11): 35-37
[79]李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社, 2005
[80]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[J].电路与系统学报, 2004, 4(2): 54-57
[81] M. Catelani, A. Fort. Fault diagnosis of electronic analog circuits using a radial basis function network classifier[J]. Measurement, 2000, 28(3): 147-158
[82] M. Catelani, A. Fort. Soft fault detection and isolation in analog circuits: some results and a comparison between a fuzzy approach and radial basis function networks[J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51(2): 196-202
[83]韩晓静,王友仁,崔江.基于小波径向基网络的电力电子电路故障诊断[J].微电子学, 2008, 38(3): 309-311
[84]李晴,何怡刚,包伟.免疫蚂蚁算法优化的RBF网络用于模拟电路故障诊断[J].仪器仪表学报, 2010, 31(6): 1255-1261
[85] F. Aminian, M. Aminian. Fault diagnosis of analog circuits using bayesian neural networks with wavelet transform as preprocessor[J]. Journal of Electronic Testing: Theory and Applications, 2001, 17(1): 29-36
[86]谢宏.基于Volterra级数、小波分析与神经网络的非线性网络故障诊断方法的研究[博士学位论文].湖南:湖南大学, 2005
[87] El-Gamal, Mohamed A.. Fault location and parameter identification in analog circuits[phD dissertation]. USA: Ohio University (the college of Engineering and Technology), 1990
[88]张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000, 26(1): 32-42
[89]边肇祺,张学工,等.模式识别[M].北京:清华大学出版社, 2000
[90] C. Cortes, V.N. Vapnik. Support vector networks[J]. Machine Learning, 1995, 20(3): 273-297
[91] C. J.C. Burges. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167
[92] V. N. Vapnik. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988-999
[93] C. W. Hsu, C. J. Lin. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425
[94] D. Anguita, S. Ridella, D. Sterpi. A new method for multi-class support vector machines[C]. Proceedings of IEEE International Joint Conference on Neural Networks(IJCNN), Budapest, Hungary, 2004, 1: 407-412
[95] Y. Zhang, X. Wei, H. Jiang. One-class classifier based on SBT for analog circuit faultdiagnosis[J]. Measurement, 2008, 41(4): 371-380
[96]何学文.基于支持向量机的故障智能诊断理论与方法研究[博士学位论文].湖南:中南大学, 2004
[97]张永.基于模糊支持向量机的多类分类算法研究[博士学位论文].辽宁:大连理工大学, 2008
[98] J. A. K. Suykens, J. Vandewalle. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300
[99] B. Scho1kopf, J. C. Platt, J. C. Shawe-Taylor, et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7): 1443-1471
[100] H. G. Chew, R. E. Bogner, C. C. Lim. Dualν-support vector machine with error rate and training size biasing[C]. In proceedings of the 26th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Salt Lake City, 2001, (2): 1269-1272
[101] http://www.support-vector-machines.org/SVM_soft.html
[102] http://www.princeton.edu/~kung/ele571/571-MatLab/571svm/
[103]胡清,王荣杰,詹宜巨.基于支持向量机的电力电子电路故障诊断技术[J].中国电机工程学报, 2008, 28(12): 107-111
[104]李蓉,叶世伟,史忠植. SVM-KNN分类器-一种提高SVM分类精度的新方法[J].电子学报, 2002, 30(5): 745-748
[105]鲁昌华,汪涌,王玲飞.基于PCA和SVM的模拟电路故障诊断[J].电子测量与仪器学报, 2008, 22(3): 64-68
[106] F. Schwenker. Hierarchical support vector machines for multi-class pattern recognition[C]. Proceedings of the fourth international conference on knowledge-based intelligent engineering systems & allied technologies, 2000, Brighton, UK, 2: 561-565
[107]孙永奎,陈光礻禹,李辉.模糊聚类与SVM诊断模拟电路单软故障的方法[J].计算机辅助设计与图形学学报, 2008, 20(5): 612-617
[108] F. Takahashi, S. Abe. Decision-tree-based multi-class support vector machines[C]. Proceedings of the 9th International Conference on Neural Information Processing(ICNP), Singapore, 2002: 1418-1422, Singapore
[109]张智星,孙春在,水谷英二.神经-模糊和软计算[M].西安:西安交通大学出版社, 2000
[110] A. Wang, J. Liu, H. Li, et al. A novel algorithm for fault diagnosis of analog circuit with tolerances using improved binary-tree svms based on somnn clustering[C]. Third International Conference on Natural Computation (ICNC), Hai Kou, Hainan, China, 2007, 1: 491-496
[111]连可,黄建国,王厚军,等.一种基于遗传算法的SVM决策树多分类策略研究[J].电子学报, 2008, 36(8): 1502-1507
[112] F. Aminian, M. Aminian, H. W. Collins. Analog fault diagnosis of actual circuits using neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51(3): 544-549
[113] Yigang He, Yanghong Tan, Yichuang Sun. Fault diagnosis of analog circuits based on wavelet packets[C]. IEEE Region 10 Conference(TENCON), Thailand, 2004: 267-270
[114]谭阳红,叶佳卓.模拟电路故障诊断的小波方法[J].电子与信息学报,2006, 28(9): 1748-1751
[115]王成华,王友仁,胡志忠.现代电子技术基础(模拟部分)[M].北京:北京航空航天大学出版社, 2005
[116] M. Aminian, F. Aminian. A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor[J]. IEEE Transactions on Instrumentation and Measurement, 2007, 56(5): 1546-1554
[117] Y. He, Y. Tan, Y. Sun. Wavelet neural network approach for fault diagnosis of analogue circuits[J]. IEE Proceedings of Circuits Devices Systems, 2004, 151(4): 379-384
[118]翟永杰.基于支持向量机的故障智能诊断方法研究[博士学位论文].河北:华北电力大学, 2004
[119]易辉,宋晓峰,姜斌,等.基于结点优化的决策导向无环图支持向量机及其在故障诊断中的应用[J].自动化学报, 2010, 36(3): 427-432
[120] M. K. IYER, M. L. BUSHNELL. Effect of noise on analog circuit testing[J]. Journal of Electronic Testing: Theory and Applications, 1999, 15(1): 11-22
[121] S. Bhunia, K. Roy. Dynamic supply current testing of analog circuits using wavelet transform[C]. Proceedings of the 20th IEEE VLSI Test Symposium(VTS), Montery, CA, 2002: 302-308
[122]钱志鸿,张琳.用噪声测量方法分析模拟电路故障的研究[J].仪器仪表学报, 2005, 26(6): 650-657
[123]张超杰,贺国,梁述海.小波变换与主元分析相结合的模拟电路检测方法[J].哈尔滨工程大学学报, 2010, 31(5): 570-575
[124]程军圣,于德介,杨宇.基于内廪模态奇异值分解和支持向量机的故障诊断方法[J].自动化学报, 2006, 32(3): 475-480
[125]徐晨曦,陈光礻禹.基于响应误差矩阵SVD的软故障诊断方法[J].仪器仪表学报, 2006, 27(9): 1100-1103
[126]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[J].控制与决策, 2006, 21(10): 1134-1137, 1142
[127]崔江,王友仁,刘权.基于高阶谱与支持向量机的电力电子电路故障诊断技术[J].中国电机工程学报, 2007, 27(10): 62-66
[128]侯青剑,王宏力.一种基于EMD的模拟电路故障特征提取方法[J].系统工程与电子技术, 2009, 31(6): 1525-528
[129]唐静远,师奕兵,周龙甫,等.非线性模拟电路故障诊断的小波领袖多重分形分析方法[J].控制与决策, 2010, 25(4): 605-609
[130] L. Yuan, Y. He, J. Huang, et al. A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(3): 586-595
[131]罗慧,王友仁,崔江.基于最优分数阶傅里叶变换的模拟电路故障特征提取新方法[J].仪器仪表学报, 2009, 30(5): 997-1001
[132] M. Aminian, F. Aminian. Neural-network based analog-circuit diagnosis using wavelet transform as preprocessor[J]. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, 2000, 47(2):151-156
[133]王军锋,张维强,宋国乡.模拟电路故障诊断的多小波神经网络算法[J].电工技术学报, 2006, 21(1): 33-36
[134]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[J].计算机辅助设计与图形学学报, 2007, 19(10): 1247-1251
[135]金瑜,陈光礻禹,刘红.模拟电路故障诊断的新方法[J].仪器仪表学报, 2007, 28(10): 1870-1873
[136]于飞,田玲玲,刘喜梅,等.基于小波多分辨率分析和主元分析的电力电子电路故障诊断[J].华东理工大学学报,2006,32(9): 1113-1116,1125
[137]王承,陈光礻禹,谢永乐.基于动态电源电流测试的模拟电路故障诊断[J].系统工程与电子技术, 2005, 27(3): 413-416
[138] P. Kalpana, K. Gunavathi. Wavelet based fault detection in analog VLSI circuits using neural networks[J]. Applied Soft Computing, 2008, 8: 1592-1598
[139] V. Stopjaková, P. Malosek, M. Matej, et al. Defect detection in analog and mixed circuits by neural networks using wavelet analysis[J]. IEEE Transactions on Reliability, 2005, 54(3): 441-448
[140] M. G. Dimopoulos, A. D. Spyronasios, D. K. Papakostas, et al. Wavelet energy-based testing using supply current measurements[J]. IET Science, Measurement and Technology, 2009, 4(2): 76-85
[141]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[J].仪器仪表学报, 2007, 28(1): 90-94
[142]唐静远,师奕兵,周龙甫,等.基于交叉熵方法和支持向量机的模拟电路故障诊断[J].控制与决策, 2009, 24(9): 1416-1420
[143]许丽佳,王厚军,龙兵.一种状态监测与健康评估方法及其在模拟电路中的应用[J].计算机辅助设计与图形学学报, 2008, 20(12): 1550-1556
[144] S. G. Mallat. A theory for multi-resolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
[145] R. Kohavi, G. H. Hohn. Wrappers for feature subset selection[J]. Artificial Intelligence, 1997, 97: 273-324
[146] Y. Liu, Y. Zheng. FS_SFS: A novel feature selection method for support vector machines[J]. Pattern Recognition, 2006, 39: 1333-1345
[147]崔宇红,倪国强,陈永飞.分布式多传感器的模糊决策融合[J].激光与红外, 2005, 35(1): 61-64
[148]尉询楷,李应红,刘建勋,等.基于支持向量机的信息融合诊断方法[J].系统工程与电子技术, 2005, 27(9): 1665-1668
[149] Peng Minfang, Shen Meie, He Yigang, et al. Analog circuit diagnosis using rbf network and D-S evidential reasoning[J]. Transactions of China Electrotechnical Society, 2009, 24(8): 6-12
[150]朱剑英.智能系统非经典数学方法[M].武汉:华中科技大学出版社, 2004
[151] Y. Maidon, B. W. Jervis, P. Fouillat, et al. Using artificial neural networks or Lagrange interpolation to characterize the faults in an analog circuit: an experimental study[J]. IEEE Transactions on Instrumentation and Measurement, 1999, 48(5): 932-938
[152] Z. Czaja. A diagnosis method of analog parts of mixed-signal systems controlled by microcontrollers[J]. Measurement, 2007, 40(2): 158-170
[153] A. M. Imam, T. G. Habetler, R. G. Harley, et al. Failure prediction of electrolytic capacitor using DSP methods[C]. Proceedings of the 20th IEEE Applied Power Electronics Conference(APEC), 2005, Austin, TX, 2: 965-970
[154] Texas Instrument. TMS320LF2407 DSP Controllers. 1999
[155]刘和平,严利平,张学锋,等. TMS320LF240x DSP结构、原理及应用[M].北京:北京航空航天大学出版社, 2002
[156] Maxim Integrated Products. Max125/Max126 data sheet. 2008
[157]毅刚,付平,王丽.采用数字相关法测量相位差[J].计量学报, 2000, 21(3): 216-221
[158]苏奎峰,吕强,邓志东,等. TMS320x28xxx原理与开发[M].北京:电子工业出版社, 2009
[159] Texas Instruments. TMS320F28335 Digital Signal Controller (DSC) data manual. 2007
[160] Texas Instruments. TMS320xF2833x Analog-to-Digital Converter (ADC) Module Reference Guide. 2007
[161] Texas Instruments. TMS320xF2833x DSC External Interface (XINTF) Reference Guide. 2007
[162]王珊珊,藏冽,张志航,等.程序设计语言-C[M].北京:清华大学出版社, 2007
[163] Z. Luo, Z. Shi. On electronic equipment fault diagnosis using least squares wavelet support vector machines[C]. Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA), Dalian, China, 2006: 6193-6197
[164] D. J. Sebald, J. A. Bucklew. Support vector machines and the multiple hypothesis test problem[J]. IEEE Transactions on Signal Processing, 2001, 49(11): 2865-2872
[165]杨智明.面向不平衡数据的支持向量机分类方法研究[博士学位论文].黑龙江:哈尔滨工业大学, 2009
[166]宋其江,徐敏强,王日新.基于分层有向图的航天器故障诊断[J].航空学报, 2009, 30(6): 1058-1062