基于提升小波及SVM优化的模拟电路智能故障诊断方法研究
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摘要
模拟电路测试与故障诊断在电路设计、设备生产和仪器维护中发挥着关键作用,是目前学术研究者和工程师在电子测试领域中具有挑战性的重要课题。随着电子技术的迅速发展,模拟电路的复杂度和密集度不断增长,对模拟电路运行的可靠性提出了更为严格的要求。在模拟电路测试和故障诊断中,传统的故障诊断理论和方法不能很好地解决因元件容差性、输出响应的连续性和非线性等电路固有属性而造成的故障多样性和复杂性问题,研究切合实用、高效高性能的现代智能故障诊断理论和方法尤为迫切。故障特征提取和故障的有效识别是智能故障诊断中的两个关键环节,本文以现代测试技术为基础,结合小波分析和以支持向量机为代表的智能计算技术,深入研究了模拟电路的特征提取优化选择和分类器的优化设计在模拟电路智能故障诊断中的问题。本文取得了如下的成果:
研究了小波分析的模拟电路故障识别灵敏度及故障特征优化方法。小波变换在信号分析中具有良好时频局部性,比单纯的时域或频域分析方法更适合表示故障电路的特征,但不同的小波函数在故障识别中具有不同的性能。本文提出用均方根(RMS)测度量化小波在故障测试中的灵敏度,以类可分性准则选择提取故障特征的最优小波函数,达到故障特征的优化。实验结果说明本方法可有效地提高故障的识别精度。
研究了基于小波神经网络的模拟电路故障诊断方法的优化问题。针对传统的神经网络方法在模拟电路故障诊断中存在收敛速度慢、易于陷入局部极小的不足,将小波分析与神经网络结合,构建小波神经网络的模拟电路的故障诊断模型,利用小波函数具有的多分辨性等特点有效地提高了网络的收敛速度和降低了误诊率。为了解决网络结构冗余而造成训练收敛方向偏离全局最优点,使推广能力降低的问题,提出用遗传算法对小波神经网络的结构和参数进行优化的方法,该方法可获得小波神经网络的最佳结构,并提高了故障的诊断效率。
研究了提升小波-支持向量机的模拟电路故障诊断方法。由于小波神经网络在故障类型数目较大时,故障的分类和诊断受网络结构复杂性和样本复杂性的影响较大,提出了基于支持向量机(SVM)的故障诊断方法。支持向量机在处理小样本学习、样本的非线性和高维模式识别方面具有优势,极大地减少故障分类的计算时间。同时,将不受傅立叶分析限制、易实现整数小波分解的提升小波分析方法用于模拟电路的特征提取,能够精确地反应故障响应信号的特征信息。采用提升小波变换(Lifting Wavelet Transform,LWT)优化故障特征,可获取分类性能更高的故障特征向量。LWT和SVM的有效结合,可获得很好的故障诊断精度和效率。
研究了二叉决策树支持向量机(DBT-SVM)优化的模拟电路层级故障诊断方法。支持向量机是模拟电路故障诊断的有效方法,但其用于模拟电路多故障诊断时,多分类扩展策略与诊断的效率、正确率密切相关。当故障类型数目较多及存在不同故障模式的特征向量相似度高甚至混叠的情况下,常规策略构造的多分类SVM因未考虑故障特征的聚类特性,在故障的误识率增加的同时诊断效率也下降。DBT-SVM在减少不可分区域及提高分类效率方面有一定的优势,但需要对结构进行优化才能获得更好的性能。本文研究了两种DBT-SVM优化方法,分别用最小生成树SVM(MST-SVM)和故障特征融合的模糊聚类二叉树(FDBT-SVM)方法优化SVM多分类的组合策略,根据故障类之间的可分性测度,形成故障子类之间具有较大分类间隔的DBT-SVM,优化的DBT-SVM方法解决了不可分故障区域问题,有效地提高故障诊断的准确率和效率。
Analog circuit testing and fault diagnosis play key roles in many fields such as circuit design, equipment manufacture and instrument maintenance. Currently, the study on the two aspects is still a challenging topic for academic researchers and engineers in electronic testing area. As the electronic technology develops rapidly, complexity and integration scale on analog circuits are increased simultaneously. More strict requirements for running reliability are desired also. It is difficult for traditional fault diagnosis theories and methods to solve the problems of fault diversity and complicacy, which are caused by continuous response, nonlinearity, and tolerance on component parameters due to the characteristics of circuits. Intensive study on intelligent fault diagnosis theories and methods with practicality and high performance is an urgent task. The fault feature extraction and effective fault identification methods are two critical parts for intelligent fault diagnosis. The optimization of feature extraction and design for classifiers for analog circuit fault diagnosis system are discussed deeply in this dissertation, based on modern testing technology with combination of wavelet analysis and support vector machine (SVM), which typically represent intelligent computing techniques. The main research contents and achievements are summarized as following:
Wavelet analysis based analog circuit fault detecting sensitivity and feature extraction optimization method are studied. Good localization on time-frequency domain of wavelet transformation provides better feature representation for faulty circuits than unitary time or frequency domain analysis. However, different wavelets present diverse resolution for fault recognition. Root mean square (RMS) score is proposed for measuring fault detecting sensitivity of wavelets for analog circuits in this paper. According to separating distance for classification, a measure criterion is presented for optimal wavelet basis selection, which leads to acquire better fault feature extraction. The experimental results show that this method can effectively improve fault identification accuracy.
Optimized analog circuit fault diagnosis approach based on wavelet neural network is researched. Aiming at the fact that conventional BP neural network usually converge to local minimum in fault diagnosis, wavelet analysis and neural network are combined to construct wavelet neural network (WNN) frame for fault diagnosis. WNN achieves high convergence speed and low fault diagnosis error rate due to the multi-resolution property of wavelet function with adaptive learning algorithm. To avoid the network gegenerality degradation caused by training convergence direction deviating from globally optimal point for structure redundance, genetic algorithm is used to optimize the structure and parameters of the network. Optimal WNN is obtained with simplified structure and higher fault diagnosis efficiency.
Lifting wavelet transform and support vector machines based analog circuit fault diagnosis method is studied thirdly. As WNN structure and pattern complexity greatly impact the results of fault classification and diagnosis under condition of large number of fault categories, support vector machine (SVM) algorithm is proposed to fault diagnosis. SVMs outperform neural network in many ways such as learning ability for small number of patterns, tackling nonlinearity and high dimension pattern recognition. It shows less time-consumption for computing in the same instance. Simultaneously, lifting wavelet analysis method, which is independent of Fourier analysis and easy to implement integral wavelet analysis, is applied to fault feature extraction of analog circuits for precise expression of faulty information. Lifting wavelet transform (LWT) presents better fault feature quality with higher partition characteristic. The presented LWT-SVM fault diagnosis approach acquires higher performance on diagnosis veracity and efficiency than neural network.
Optimized binary tree decision SVM (DBT-SVM) based hierarchical fault diagnosis methods for analog circuits are researched. SVMs are proved as an effective approach for fault diagnosis. When SVMs are used for multi-fault diagnosis in analog circuits, the diagnosis accuracy and efficiency largely depend on the extension strategies for multi-classification. On condition that there are large numbers of fault categories and high similarity even overlapping among various fault classes occur, conventional SVM combination strategies for fault diagnosis cannot get good results on fault recognition accuracy and efficiency due to not considering the clustering properties of fault features. DBT-SVM is able to decrease inseparable area to gain classification performance improvement, but it need structure optimization. This paper studies two ways for optimized DBT-SVM methods: minimum spanning trees SVM and fuzzy clustering optimized DBT-SVM (FDBT-SVM) with wavelet fault feature fusion. According to the separability measure of fault classes, multi-classification DBT-SVMs with large margins are constituted to avoid the inseparable fault area. The presented methods largely enhance fault diagnosis accuracy and efficiency comparing with conventional multi-classification SVM algorithms.
研究了小波分析的模拟电路故障识别灵敏度及故障特征优化方法。小波变换在信号分析中具有良好时频局部性,比单纯的时域或频域分析方法更适合表示故障电路的特征,但不同的小波函数在故障识别中具有不同的性能。本文提出用均方根(RMS)测度量化小波在故障测试中的灵敏度,以类可分性准则选择提取故障特征的最优小波函数,达到故障特征的优化。实验结果说明本方法可有效地提高故障的识别精度。
研究了基于小波神经网络的模拟电路故障诊断方法的优化问题。针对传统的神经网络方法在模拟电路故障诊断中存在收敛速度慢、易于陷入局部极小的不足,将小波分析与神经网络结合,构建小波神经网络的模拟电路的故障诊断模型,利用小波函数具有的多分辨性等特点有效地提高了网络的收敛速度和降低了误诊率。为了解决网络结构冗余而造成训练收敛方向偏离全局最优点,使推广能力降低的问题,提出用遗传算法对小波神经网络的结构和参数进行优化的方法,该方法可获得小波神经网络的最佳结构,并提高了故障的诊断效率。
研究了提升小波-支持向量机的模拟电路故障诊断方法。由于小波神经网络在故障类型数目较大时,故障的分类和诊断受网络结构复杂性和样本复杂性的影响较大,提出了基于支持向量机(SVM)的故障诊断方法。支持向量机在处理小样本学习、样本的非线性和高维模式识别方面具有优势,极大地减少故障分类的计算时间。同时,将不受傅立叶分析限制、易实现整数小波分解的提升小波分析方法用于模拟电路的特征提取,能够精确地反应故障响应信号的特征信息。采用提升小波变换(Lifting Wavelet Transform,LWT)优化故障特征,可获取分类性能更高的故障特征向量。LWT和SVM的有效结合,可获得很好的故障诊断精度和效率。
研究了二叉决策树支持向量机(DBT-SVM)优化的模拟电路层级故障诊断方法。支持向量机是模拟电路故障诊断的有效方法,但其用于模拟电路多故障诊断时,多分类扩展策略与诊断的效率、正确率密切相关。当故障类型数目较多及存在不同故障模式的特征向量相似度高甚至混叠的情况下,常规策略构造的多分类SVM因未考虑故障特征的聚类特性,在故障的误识率增加的同时诊断效率也下降。DBT-SVM在减少不可分区域及提高分类效率方面有一定的优势,但需要对结构进行优化才能获得更好的性能。本文研究了两种DBT-SVM优化方法,分别用最小生成树SVM(MST-SVM)和故障特征融合的模糊聚类二叉树(FDBT-SVM)方法优化SVM多分类的组合策略,根据故障类之间的可分性测度,形成故障子类之间具有较大分类间隔的DBT-SVM,优化的DBT-SVM方法解决了不可分故障区域问题,有效地提高故障诊断的准确率和效率。
Analog circuit testing and fault diagnosis play key roles in many fields such as circuit design, equipment manufacture and instrument maintenance. Currently, the study on the two aspects is still a challenging topic for academic researchers and engineers in electronic testing area. As the electronic technology develops rapidly, complexity and integration scale on analog circuits are increased simultaneously. More strict requirements for running reliability are desired also. It is difficult for traditional fault diagnosis theories and methods to solve the problems of fault diversity and complicacy, which are caused by continuous response, nonlinearity, and tolerance on component parameters due to the characteristics of circuits. Intensive study on intelligent fault diagnosis theories and methods with practicality and high performance is an urgent task. The fault feature extraction and effective fault identification methods are two critical parts for intelligent fault diagnosis. The optimization of feature extraction and design for classifiers for analog circuit fault diagnosis system are discussed deeply in this dissertation, based on modern testing technology with combination of wavelet analysis and support vector machine (SVM), which typically represent intelligent computing techniques. The main research contents and achievements are summarized as following:
Wavelet analysis based analog circuit fault detecting sensitivity and feature extraction optimization method are studied. Good localization on time-frequency domain of wavelet transformation provides better feature representation for faulty circuits than unitary time or frequency domain analysis. However, different wavelets present diverse resolution for fault recognition. Root mean square (RMS) score is proposed for measuring fault detecting sensitivity of wavelets for analog circuits in this paper. According to separating distance for classification, a measure criterion is presented for optimal wavelet basis selection, which leads to acquire better fault feature extraction. The experimental results show that this method can effectively improve fault identification accuracy.
Optimized analog circuit fault diagnosis approach based on wavelet neural network is researched. Aiming at the fact that conventional BP neural network usually converge to local minimum in fault diagnosis, wavelet analysis and neural network are combined to construct wavelet neural network (WNN) frame for fault diagnosis. WNN achieves high convergence speed and low fault diagnosis error rate due to the multi-resolution property of wavelet function with adaptive learning algorithm. To avoid the network gegenerality degradation caused by training convergence direction deviating from globally optimal point for structure redundance, genetic algorithm is used to optimize the structure and parameters of the network. Optimal WNN is obtained with simplified structure and higher fault diagnosis efficiency.
Lifting wavelet transform and support vector machines based analog circuit fault diagnosis method is studied thirdly. As WNN structure and pattern complexity greatly impact the results of fault classification and diagnosis under condition of large number of fault categories, support vector machine (SVM) algorithm is proposed to fault diagnosis. SVMs outperform neural network in many ways such as learning ability for small number of patterns, tackling nonlinearity and high dimension pattern recognition. It shows less time-consumption for computing in the same instance. Simultaneously, lifting wavelet analysis method, which is independent of Fourier analysis and easy to implement integral wavelet analysis, is applied to fault feature extraction of analog circuits for precise expression of faulty information. Lifting wavelet transform (LWT) presents better fault feature quality with higher partition characteristic. The presented LWT-SVM fault diagnosis approach acquires higher performance on diagnosis veracity and efficiency than neural network.
Optimized binary tree decision SVM (DBT-SVM) based hierarchical fault diagnosis methods for analog circuits are researched. SVMs are proved as an effective approach for fault diagnosis. When SVMs are used for multi-fault diagnosis in analog circuits, the diagnosis accuracy and efficiency largely depend on the extension strategies for multi-classification. On condition that there are large numbers of fault categories and high similarity even overlapping among various fault classes occur, conventional SVM combination strategies for fault diagnosis cannot get good results on fault recognition accuracy and efficiency due to not considering the clustering properties of fault features. DBT-SVM is able to decrease inseparable area to gain classification performance improvement, but it need structure optimization. This paper studies two ways for optimized DBT-SVM methods: minimum spanning trees SVM and fuzzy clustering optimized DBT-SVM (FDBT-SVM) with wavelet fault feature fusion. According to the separability measure of fault classes, multi-classification DBT-SVMs with large margins are constituted to avoid the inseparable fault area. The presented methods largely enhance fault diagnosis accuracy and efficiency comparing with conventional multi-classification SVM algorithms.
引文
[1]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] Li F, Woo P-Y. 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.
[3] Roy A, Sunter S, Fudoli A, et al. High accuracy stimulus generation for A/D converter BIST. Proceedings of International Conference on Test, 2002:1031-1039.
[4] Variyam P N, Chatterjee A. Enhancing test effectiveness for analog circuits using synthesized measurements. Proceedings of 16th IEEE Symposium on VLSI Test 1998:132-137.
[5] Wang L, Wu C, Wen X. VLSI test principles and architectures design for testability: Morgan Kaufmann Publishers, 2006.
[6] Vinnakota B. Analog and mixed-signal test: Prentice Hall 1998.
[7] Kabisatpathy P, Barua A, Sinha S. Fault diagnosis of analog integrated circuits: Springer, 2005.
[8] Bushnell M L, Agrawal V D. Essential of electronic testing for digital, memory and mixed-signal VLSI circuits: Springer, 2000.
[9] Bandler J W, Salama A E. Fault diagnosis of analog circuits. Proceedings of the IEEE, 1985, 73(8): 1279-1325.
[10] Berkowitz R S. Condition for network-element-value solvability. IRE Transactions on Circuits Theory, 1962,9(1):24-29.
[11] 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.
[12] Wu C-C, Nakajima K, Wey C-L, et al. Analog fault diagnosis with failure bounds. IEEE Transaction on Circuits and Systems 1982, 29(5): 277-284.
[13] Huang Z, Lin C, Liu R. Node-fault diagnosis and a design of testability. IEEE Transactions on Circuits and Systems, 1983, 30(5): 257-265.
[14] Bandler J W, Salama A E. Fault diagnosis of analog circuits. Proceedings of the IEEE, 1985, 73(8): 1279-1325.
[15] 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.
[16] Zou R, Huang J M. Fault location of linear nonreciprocal circuit with tolerance. IEEE International Symposium on Circuits and Systems, 1988, 2:1163-1166.
[17] 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.
[18] Spain R, Upadhyaya S. Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Transactions on Circuits and SystemsⅡ: Analog and Digital Signal Processing, 1997, 44(3): 188-196.
[19] Zou R, Huang J M. Fault location of linear nonreciprocal circuit with tolerance. IEEE International Symposium on Circuits and Systems, 1988, 2:1163-1166.
[20] Jiang B L, Wey C L. Fault prediction process for analog circuit networks. IJCTA, 1989, 17:141-149.
[21] Hocchwald W, Bastian J O. A DC approach for analog fault dictionary determination. IEEE Transactions on CAS, 1979, CAS-26: 523-529.
[22] Lin P, Elcherif Y. Analog circuits fault dictionary——new approaches and implantation. Int. J. of Circuit Theory and Application, 1985, 13: 149-172.
[23] Favalli M, Olivo P, Ricco B. A probabilistic fault model for "analog" faults in digital CMOS circuits. IEEE Transactions on Computer-aided Design, 1992, (11):1459-1462.
[24] He Y, Sun Y. Neural network-based L1-norm optimisation approach for fault diagnosis of nonlinear circuits with tolerance. IEE Proceedings of Circuits, Devices and Systems, 2001, 148(4): 223-228.
[25] Satyanarayana T, Subramanyam G, Rao K V R. Implementing an expert system for fault diagnosis of electronic equipment. Engineering Applications of Artificial Intelligence, 1995, 8(3): 355-364.
[26] 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.
[27] Catelani M, Fort A, Alippi C. A fuzzy approach for soft fault detection in analog circuits. Measurement, 2002, 32(1): 73-83.
[28] El-Gamal M A, Abdulghafour M. Fault isolation in analog circuits using a fuzzy inference system. Computers & Electrical Engineering, 2003, 29(1): 213-229.
[29] Wang P, Yang S. Soft fault test and diagnosis for analog circuits. IEEE International Symposium on Circuits and Systems, 2005:2188-2191.
[30] Wang P, Yang S. A new diagnosis approach for handling tolerance in analog and mixed-signalcircuits by using fuzzy math. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 2005, 52(10): 2118-2127.
[31] Starzyk J A, El-Gamal M A. Artificial neural network for testing analog circuits. IEEE International Symposium on Circuits and Systems, 1990, 3:1851-1854.
[32] Rajan V, Yang J, et al. Machine learning algorithms for fault diagnosis in analog circuits. 1998 IEEE International Conference on Systems, Man, and Cybernetics, 1998: 1874-1879
[33] 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.
[34]崔莼,罗先觉,邱关源.故障诊断交流字典法的前向神经网络实现方法.微电子学, 1996, 26(5):313-318.
[35] El-Gamal M A, El-Yazeed M F A. A combined clustering and neural network approach for analog multiple hard fFault classification. Journal of Electronic Testing: theory and applications, 1999, 14(3): 207-217.
[36] 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.
[37] Stopjakova V, Malosek P. Classification of defective analog integrated circuits using artificial neural networks. Journal of Electronic Testing: Theory and Applications, 2004, (20) :25-37.
[38] 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.
[39] Litovski V, Anderjevic M, Zwolinski M. Analogue electronic circuit diagnosis based on ANN. Microelectronics and Reliability, 2006, 46(8):1382-1391.
[40] Deng Y, He Y, Sun Y. Fault diagnosis of analog circuits with tolerances using artificial neural networks. Proceedings IEEE APCCAS, 2000: 292-295.
[41]何怡刚,梁戈超.模拟电路故障诊断的BP神经网络方法.湖南大学学报(自然科学版), 2003, 30(5): 35-39.
[42] Catelani M, Fort A. Fault diagnosis of electronic analog circuits using a radial basis function network classifier. Measurement, 2000, 28(3): 147-158.
[43] 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.
[44]王承,陈光礻禹,谢永乐.基于径向基函数神经网络的模拟/混合电路故障诊断.电路与系统学报, 2007, 12(2): 65-68.
[45]殷时蓉,陈光礻禹,谢永乐.应用Elman网络优化非线性模拟电路测试激励.电子科技大学学报, 2008, 37(4):574-577.
[46]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[博士学位论文].长沙:湖南大学, 2006.
[47] Contu S, Fanni A, Marchesi M, et al. Wavelet analysis for diagnostic problems. 8th Mediterranean Electro technical Conference, 1996, 3:157-1574.
[48] Aminian M, Aminian F. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Transactions onCircuits and Systems II: Analog and Digital Signal Processing, 2000, 47(2): 151-156.
[49] Aminian F, Aminian M. Fault diagnosis of analog circuits using bayesian neural networks with wavelet transform as preprocessor. Journal of electronic Testing: Theory and Applications, 2001(17): 29-36.
[50] Swarup B, Kaushik R. Dynamic supply current testing for analog circuits using wavelet transform. Proceeding 20th IEEE VLSI Symposium, 2002: 302-307.
[51] Swarup B, Arijit R, Kaushik R. Frequency specification testing of analog filters using wavelet transform of dynamic supply current. 5th International Symposium on Quality Electronic Design, 2004: 389-394.
[52] 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.
[53] Maehran A, Farzan A. A modular fault-diagnostic system for analog electronic circuits using neural net-works with wavelet transform as a preprocessor. IEEE Transactions On Instrumentation and Measurement, 2007, 56(5): 1546-1554.
[54] He Y, Tan Y, Sun Y. Wavelet neural network approach for fault diagnosis of analogue circuits. IEE Proceedings on Circuits Devices System, 2004, 151(8):379-384.
[55] Grzechca D, Rutkowski J.Use 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.
[56] Torralba A, Chavez 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.
[57] Asgary R, Mohammadi K. Using fuzzy probabilistic neural network for fault detection in MEMS. 5th International Conference on Intelligent Systems Design and Applications, 2005: 136-140.
[58] Grasso F, Manetti S, Piccirilli M C. An approach to analog fault diagnosis using genetic algorithms. IEEE MELECON 2004, 2004:111-114.
[59] El-Gamal M A. Genetically evolved neural networks for fault classification in analog circuits. Neural Computing&Application, 2002, 11(2):112-121.
[60] El-Gamal M, Mohamed M. Ensembles of neural networks for fault diagnosis in analog circuits. Journal of Electronic Testing, 2007, 23(4): 323-339.
[61] Somayajula S S. A neural network approach to hierarchical analog fault diagnosis.Proceedings of IEEE Systems Readiness Technology Conference, 1993: 699-706.
[62] Salat R, Osowski S. Analog filter diagnosis using Support Vector Machine. ECCTD'03, 2003, III: 421-424.
[63] Siwek K, Osowski S, Markiewicz T. Support vector machine for fault diagnosis in electrical circuits. Proceedings of the 7th Nordic Signal Processing Symposium, 2006: 342-345.
[64]唐人亨.模拟电子系统的自动故障诊断.北京:高等教育出版社, 1991.
[65]邹锐.模拟电路故障诊断原理和方法.武汉:华中理工大学出版社, 1998.
[66]黄洁,何怡刚.模拟电路故障诊断的发展现状与展望.微电子学, 2004, 34(1):21-25.
[67]辛秀华,杨宣访,乐平.现代模拟电路故障诊断方法综述.自动化与仪器仪表, 2005,2:1-17.
[68]宋国明,王厚军,姜书艳, et al.基于神经网络的融合智能诊断方法在模拟电路故障诊断中的应用. 2007中国仪器仪表与测控技术年会, 2007: 552-555.
[69]林争辉,刘美仅,王海雄.模拟电路的故障激励论.上海交通大学学报, 1995, 29(1): 71-78.
[70]张维强,徐晨,宋国乡.模拟电路故障诊断的小波包预处理神经网络改进算法.信号处理, 2007, 23(2): 204-209.
[71]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.
[72]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.
[73]殷时蓉.基于Volterra级数和神经网络的非线性电路故障诊断研究[博士学位论文].成都:电子科技大学, 2007.
[74]陈圣俭,洪炳熔,王月芳, et al.可诊断容差模拟电路软故障的新故障字典法.电子学报, 2000, 28(2): 127-129.
[75]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.
[76]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.
[77]师宇杰,张耀升.模拟电路故障诊断的VM验证算法.天津大学学报, 1998, 31(5):600-605.
[78]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
[79]谭阳红,何怡刚等.大规模电路故障诊断神经网络方法.湖南大学学报, 2001, 6(4): 5-28.
[80]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.
[81]马野,李楠.应用自组织映射神经网络进行模拟电路故障诊断的仿真研究.系统仿真学报, 2001, 13(5):582-584.
[82]王鹏宇,黄智刚.模糊理论与神经网络结合对模拟电路进行分层故障诊断.电子测量技术, 2002, (1):7-9.
[83]彭刚,彭敏放,何怡刚.基于聚类模糊神经网络的非线性电路故障诊断.微电子学与计算机, 2006, 23(8):1-3.
[84]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.
[85]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.
[86]罗晓峰,王友仁.基于信息融合的神经网络模拟电路故障诊断研究.计算机测量与控制, 2006, 14(2):146-148.
[87]彭敏放,何怡刚,王耀南, et al.模拟电路的融合智能故障诊断.中国电机工程学报, 2006, 26(3): 19-24.
[88] He Y, Tan Y, Sun Y. Fault diagnosis of analog circuits based on wavelet packets. TENCON 2004 IEEE Region 10 Conference, 2004:267-270.
[89]谭阳红,何怡刚.模拟电路故障诊断的小波方法.电工技术学报, 2005, 20(8):89-93.
[90]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.
[91]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.电工技术学报, 2006, 21(1): 33-36.
[92] Zhou J, Xu H, Wang H. A novel fault diagnosis based on wavelet transform of IDDT waveform.2004 International Conference on Communications, Circuits and Systems, 2004: 1320-1324.
[93]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.
[94]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.
[95] Song G, Wang H, Liu H , Jiang S. Fault diagnosis of analog circuits based on wavelet neural network. DCABES 2006 Proceedings, 2006: 803-807.
[96] Song G, Wang H, Jiang S, Liu H. Fault diagnosis approach of analog circuits based on genetic wavelet neural network. International Conference on Electronic Measurement and Instruments, 2007: 3-675-3-679.
[97]刘红,陈光礻禹,宋国明, et al.基于AdaBoost集成网络的模拟电路单软故障诊断.仪器仪表学报, 2010, 31(4): 851-856.
[98]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.北京航空航天大学学报, 2003, 29(9):789-792.
[99]罗志勇,史忠科.一种模拟电路的支持向量机故障诊断方法.计算机工程, 2006, 32(15): 34-36.
[100]谢佑川,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):167-171.
[101] Wang A, Liu J, Wu J. Research on soft fault diagnosis algorithm of analogy circuits based on DDAGSVMs. Proceeding of the 2007 IEEE International Conference on Integration Technology, 2007, 3:499-503.
[102]孙永奎,陈光礻禹,李辉.支持向量机在模拟电路故障诊断中的应用.电子测量与仪器学报, 2008, 22(2):72-75.
[103]孙永奎,陈光礻禹,李辉.基于可测性分析和支持向量机的模拟电路故障诊断.仪器仪表学报, 2008, 29(6): 1182-1186.
[104]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.仪器仪表学报, 2008, 29(6): 1216-1220.
[105] Long B, Huang J, Tian S. Least squares support vector machine based analog circuit fault diagnosis using wavelet transform as preprocessor. 2008 International Conference on Communications, Circuits and Systems Proceedings, 2008: 1026 -1029.
[106] 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.
[107] Daubechies I. Ten Lecture Notes on Wavelets. Philadelphia: SIAM, 1992.
[108]孙延奎.小波分析及其应用.北京:机械工业出版社, 2005.
[109]张贤达.现代信号处理(第二版).北京:清华大学出版社, 2002.
[110] Kaminska B, Arabi K,Bell I,et al.Analog and mixed signal benchmark circuits first release. proceedings of International Test Conference,1997:183-190.
[111] Zhang Q H, Benveniste A.Wavelet networks. IEEE Transactions on Neural Networks, 1992, 3(6):889-898.
[112]李弼程,罗建书.小波分析及其应用.北京:电子工业出版社, 2003.
[113] Daubechies I. The wavelet transform,time-frequency localization and signal analysis. IEEE Transaction Information Theory, 1990, 36(5):961~1005.
[114] Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks, 1989 2(7): 183-192.
[115] Michalewiz, Z. Genetic algorithm + data structure = evolution programs (3rd edition), Springer-Verlag, New York, 1996.
[116] Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, MIT Press, 1997, (9): 281-287.
[117] Burges C J C. A tutorial on support vector machines for aattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[118] Bredensteiner E J, Bennett K P. Multicategory classification by support vector machines. Computational Optimization and Applications, 1999, 12(1): 53-79.
[119] Li Z, Tang X. Using support vector machines to enhance the performance of bayesian face recognition. IEEE Transactions on Information Forensics and Security, 2007, 2(2): 174-180.
[120]胡寿松,王源.基于支持向量机的非线性系统故障诊断.控制与决策, 2001, 16(5):617-620
[121] Yang J, Zhang Y, Zhu Y. Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mechanical Systems and Signal Processing, 2007, 21(5): 2012-2024.
[122] Vapnik V N. The Nature of statistical learning theory. Springer-Verlag, New York, 1998.
[123] Keerthi S S, Lin C. Asymptotic behavior of support vector machines with gaussian kernel.Neural Computation, 2003, 15(7):1667~1689.
[124] Lin H, Lin C.A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods [EB/OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvm
[125] Liao H, Mrinal K. Efficient architectures for 1-D and 2-D lifting-based wavelet transforms. IEEE Transaction on Signal Processing, 2004, 52(5): 1315-1326.
[126]段晨东,何正嘉.一种基于提升小波变换的故障特征提取方法及其应用.振动与冲击, 2007, 26(2):10-13.
[127] Guo C, Wang T, Ye J, et al. Retinal images fusion based on lifting wavelet transform. 7th World Congress on Intelligent Control and Automation, 2008:8511-8515.
[128] Liu Y, King N. Weighted adaptive lifting-based wavelet transform for image coding. IEEE Transactions on Image Processing, 2008, 17(4):500-511
[129] Daubechies I, Sweldens W. Factoring wavelet transform into lifting steps. Journal of Fourier Analysis and Application, 1998, 4(3):247-269.
[130] Hsu C-W, Lin C-J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425.
[131] Cheong S, Sang H, Lee S. Support vector machines with binary tree architecture for multi-class classification. Neural Information Processing Letters and Reviews, 2004, 2(3):47-51.
[132] Schwenker F. Hierarchical support vector machines for multi-class pattern recognition. Fourth International Conference on knowledge-Based Intelligent Engineering Systems & Allied Technologies, 2000: 561-565.
[133] Vural V and Dy J. A hierarchical method for multi-class support vector machines. Twenty-First International Conference on Machine Learning, 2004: 831-838.
[134] Fei B and Liu J. Binary tree of SVM: a new fast multiclass training and classification algorithm. IEEE Transactions on Neural Networks, 2006, 17(3):696-704.
[135]马笑潇,黄席樾,柴毅.基于SVM的二叉树多类分类算法及其在故障诊断中的应用.控制与决策, 2003, 18(13):272-276.
[136]唐发明,王仲东,陈绵云.支持向量机多分类算法研究.控制与决策, 2005, 20(7):746-749.
[137]孟媛媛,刘希玉.一种新的基于二叉树的SVM多类分类方法.计算机应用, 2005, 25(11): 2653-2657.
[138] Chen J, Wang C. Combining support vector machines with a pairwise decision tree. IEEE Geoscience and Remote Sensing Letters, 2008, 5(3):409-413.
[139] Ahuja R K, Magnanti T L, Orlin J B. Network flows: theory, algorithm, and applications (English Version) [M]. Beijing: Mechanical Industry Press, 2005: 510-536.
[140]肖俊,庄挺越,吴飞.基于层次细节与最小生成树的三维地形识别与检索.软件学报, 2003, 14(11):1955-1963.
[141]李朝健,李朝鹏,李肯立.基于最小生成树的并行分层聚类算法.微电子学与计算机, 2008, 25(9):196-198.
[142] Manevitz L and Yousef M. One-class SVMs for document classification. The Journal of Machine Learning Research, 2002, vol. 2: 139-154.
[143] Marcellin S, Zighed D, Ritschard G. Evaluating decision trees grown with asymmetric entropies. 17th International Symposium on Methodologies for Intelligent Systems, 2008:56-67.
[2] Li F, Woo P-Y. 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.
[3] Roy A, Sunter S, Fudoli A, et al. High accuracy stimulus generation for A/D converter BIST. Proceedings of International Conference on Test, 2002:1031-1039.
[4] Variyam P N, Chatterjee A. Enhancing test effectiveness for analog circuits using synthesized measurements. Proceedings of 16th IEEE Symposium on VLSI Test 1998:132-137.
[5] Wang L, Wu C, Wen X. VLSI test principles and architectures design for testability: Morgan Kaufmann Publishers, 2006.
[6] Vinnakota B. Analog and mixed-signal test: Prentice Hall 1998.
[7] Kabisatpathy P, Barua A, Sinha S. Fault diagnosis of analog integrated circuits: Springer, 2005.
[8] Bushnell M L, Agrawal V D. Essential of electronic testing for digital, memory and mixed-signal VLSI circuits: Springer, 2000.
[9] Bandler J W, Salama A E. Fault diagnosis of analog circuits. Proceedings of the IEEE, 1985, 73(8): 1279-1325.
[10] Berkowitz R S. Condition for network-element-value solvability. IRE Transactions on Circuits Theory, 1962,9(1):24-29.
[11] 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.
[12] Wu C-C, Nakajima K, Wey C-L, et al. Analog fault diagnosis with failure bounds. IEEE Transaction on Circuits and Systems 1982, 29(5): 277-284.
[13] Huang Z, Lin C, Liu R. Node-fault diagnosis and a design of testability. IEEE Transactions on Circuits and Systems, 1983, 30(5): 257-265.
[14] Bandler J W, Salama A E. Fault diagnosis of analog circuits. Proceedings of the IEEE, 1985, 73(8): 1279-1325.
[15] 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.
[16] Zou R, Huang J M. Fault location of linear nonreciprocal circuit with tolerance. IEEE International Symposium on Circuits and Systems, 1988, 2:1163-1166.
[17] 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.
[18] Spain R, Upadhyaya S. Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Transactions on Circuits and SystemsⅡ: Analog and Digital Signal Processing, 1997, 44(3): 188-196.
[19] Zou R, Huang J M. Fault location of linear nonreciprocal circuit with tolerance. IEEE International Symposium on Circuits and Systems, 1988, 2:1163-1166.
[20] Jiang B L, Wey C L. Fault prediction process for analog circuit networks. IJCTA, 1989, 17:141-149.
[21] Hocchwald W, Bastian J O. A DC approach for analog fault dictionary determination. IEEE Transactions on CAS, 1979, CAS-26: 523-529.
[22] Lin P, Elcherif Y. Analog circuits fault dictionary——new approaches and implantation. Int. J. of Circuit Theory and Application, 1985, 13: 149-172.
[23] Favalli M, Olivo P, Ricco B. A probabilistic fault model for "analog" faults in digital CMOS circuits. IEEE Transactions on Computer-aided Design, 1992, (11):1459-1462.
[24] He Y, Sun Y. Neural network-based L1-norm optimisation approach for fault diagnosis of nonlinear circuits with tolerance. IEE Proceedings of Circuits, Devices and Systems, 2001, 148(4): 223-228.
[25] Satyanarayana T, Subramanyam G, Rao K V R. Implementing an expert system for fault diagnosis of electronic equipment. Engineering Applications of Artificial Intelligence, 1995, 8(3): 355-364.
[26] 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.
[27] Catelani M, Fort A, Alippi C. A fuzzy approach for soft fault detection in analog circuits. Measurement, 2002, 32(1): 73-83.
[28] El-Gamal M A, Abdulghafour M. Fault isolation in analog circuits using a fuzzy inference system. Computers & Electrical Engineering, 2003, 29(1): 213-229.
[29] Wang P, Yang S. Soft fault test and diagnosis for analog circuits. IEEE International Symposium on Circuits and Systems, 2005:2188-2191.
[30] Wang P, Yang S. A new diagnosis approach for handling tolerance in analog and mixed-signalcircuits by using fuzzy math. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 2005, 52(10): 2118-2127.
[31] Starzyk J A, El-Gamal M A. Artificial neural network for testing analog circuits. IEEE International Symposium on Circuits and Systems, 1990, 3:1851-1854.
[32] Rajan V, Yang J, et al. Machine learning algorithms for fault diagnosis in analog circuits. 1998 IEEE International Conference on Systems, Man, and Cybernetics, 1998: 1874-1879
[33] 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.
[34]崔莼,罗先觉,邱关源.故障诊断交流字典法的前向神经网络实现方法.微电子学, 1996, 26(5):313-318.
[35] El-Gamal M A, El-Yazeed M F A. A combined clustering and neural network approach for analog multiple hard fFault classification. Journal of Electronic Testing: theory and applications, 1999, 14(3): 207-217.
[36] 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.
[37] Stopjakova V, Malosek P. Classification of defective analog integrated circuits using artificial neural networks. Journal of Electronic Testing: Theory and Applications, 2004, (20) :25-37.
[38] 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.
[39] Litovski V, Anderjevic M, Zwolinski M. Analogue electronic circuit diagnosis based on ANN. Microelectronics and Reliability, 2006, 46(8):1382-1391.
[40] Deng Y, He Y, Sun Y. Fault diagnosis of analog circuits with tolerances using artificial neural networks. Proceedings IEEE APCCAS, 2000: 292-295.
[41]何怡刚,梁戈超.模拟电路故障诊断的BP神经网络方法.湖南大学学报(自然科学版), 2003, 30(5): 35-39.
[42] Catelani M, Fort A. Fault diagnosis of electronic analog circuits using a radial basis function network classifier. Measurement, 2000, 28(3): 147-158.
[43] 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.
[44]王承,陈光礻禹,谢永乐.基于径向基函数神经网络的模拟/混合电路故障诊断.电路与系统学报, 2007, 12(2): 65-68.
[45]殷时蓉,陈光礻禹,谢永乐.应用Elman网络优化非线性模拟电路测试激励.电子科技大学学报, 2008, 37(4):574-577.
[46]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[博士学位论文].长沙:湖南大学, 2006.
[47] Contu S, Fanni A, Marchesi M, et al. Wavelet analysis for diagnostic problems. 8th Mediterranean Electro technical Conference, 1996, 3:157-1574.
[48] Aminian M, Aminian F. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Transactions onCircuits and Systems II: Analog and Digital Signal Processing, 2000, 47(2): 151-156.
[49] Aminian F, Aminian M. Fault diagnosis of analog circuits using bayesian neural networks with wavelet transform as preprocessor. Journal of electronic Testing: Theory and Applications, 2001(17): 29-36.
[50] Swarup B, Kaushik R. Dynamic supply current testing for analog circuits using wavelet transform. Proceeding 20th IEEE VLSI Symposium, 2002: 302-307.
[51] Swarup B, Arijit R, Kaushik R. Frequency specification testing of analog filters using wavelet transform of dynamic supply current. 5th International Symposium on Quality Electronic Design, 2004: 389-394.
[52] 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.
[53] Maehran A, Farzan A. A modular fault-diagnostic system for analog electronic circuits using neural net-works with wavelet transform as a preprocessor. IEEE Transactions On Instrumentation and Measurement, 2007, 56(5): 1546-1554.
[54] He Y, Tan Y, Sun Y. Wavelet neural network approach for fault diagnosis of analogue circuits. IEE Proceedings on Circuits Devices System, 2004, 151(8):379-384.
[55] Grzechca D, Rutkowski J.Use 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.
[56] Torralba A, Chavez 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.
[57] Asgary R, Mohammadi K. Using fuzzy probabilistic neural network for fault detection in MEMS. 5th International Conference on Intelligent Systems Design and Applications, 2005: 136-140.
[58] Grasso F, Manetti S, Piccirilli M C. An approach to analog fault diagnosis using genetic algorithms. IEEE MELECON 2004, 2004:111-114.
[59] El-Gamal M A. Genetically evolved neural networks for fault classification in analog circuits. Neural Computing&Application, 2002, 11(2):112-121.
[60] El-Gamal M, Mohamed M. Ensembles of neural networks for fault diagnosis in analog circuits. Journal of Electronic Testing, 2007, 23(4): 323-339.
[61] Somayajula S S. A neural network approach to hierarchical analog fault diagnosis.Proceedings of IEEE Systems Readiness Technology Conference, 1993: 699-706.
[62] Salat R, Osowski S. Analog filter diagnosis using Support Vector Machine. ECCTD'03, 2003, III: 421-424.
[63] Siwek K, Osowski S, Markiewicz T. Support vector machine for fault diagnosis in electrical circuits. Proceedings of the 7th Nordic Signal Processing Symposium, 2006: 342-345.
[64]唐人亨.模拟电子系统的自动故障诊断.北京:高等教育出版社, 1991.
[65]邹锐.模拟电路故障诊断原理和方法.武汉:华中理工大学出版社, 1998.
[66]黄洁,何怡刚.模拟电路故障诊断的发展现状与展望.微电子学, 2004, 34(1):21-25.
[67]辛秀华,杨宣访,乐平.现代模拟电路故障诊断方法综述.自动化与仪器仪表, 2005,2:1-17.
[68]宋国明,王厚军,姜书艳, et al.基于神经网络的融合智能诊断方法在模拟电路故障诊断中的应用. 2007中国仪器仪表与测控技术年会, 2007: 552-555.
[69]林争辉,刘美仅,王海雄.模拟电路的故障激励论.上海交通大学学报, 1995, 29(1): 71-78.
[70]张维强,徐晨,宋国乡.模拟电路故障诊断的小波包预处理神经网络改进算法.信号处理, 2007, 23(2): 204-209.
[71]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.
[72]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.
[73]殷时蓉.基于Volterra级数和神经网络的非线性电路故障诊断研究[博士学位论文].成都:电子科技大学, 2007.
[74]陈圣俭,洪炳熔,王月芳, et al.可诊断容差模拟电路软故障的新故障字典法.电子学报, 2000, 28(2): 127-129.
[75]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.
[76]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.
[77]师宇杰,张耀升.模拟电路故障诊断的VM验证算法.天津大学学报, 1998, 31(5):600-605.
[78]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
[79]谭阳红,何怡刚等.大规模电路故障诊断神经网络方法.湖南大学学报, 2001, 6(4): 5-28.
[80]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.
[81]马野,李楠.应用自组织映射神经网络进行模拟电路故障诊断的仿真研究.系统仿真学报, 2001, 13(5):582-584.
[82]王鹏宇,黄智刚.模糊理论与神经网络结合对模拟电路进行分层故障诊断.电子测量技术, 2002, (1):7-9.
[83]彭刚,彭敏放,何怡刚.基于聚类模糊神经网络的非线性电路故障诊断.微电子学与计算机, 2006, 23(8):1-3.
[84]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.
[85]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.
[86]罗晓峰,王友仁.基于信息融合的神经网络模拟电路故障诊断研究.计算机测量与控制, 2006, 14(2):146-148.
[87]彭敏放,何怡刚,王耀南, et al.模拟电路的融合智能故障诊断.中国电机工程学报, 2006, 26(3): 19-24.
[88] He Y, Tan Y, Sun Y. Fault diagnosis of analog circuits based on wavelet packets. TENCON 2004 IEEE Region 10 Conference, 2004:267-270.
[89]谭阳红,何怡刚.模拟电路故障诊断的小波方法.电工技术学报, 2005, 20(8):89-93.
[90]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.
[91]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.电工技术学报, 2006, 21(1): 33-36.
[92] Zhou J, Xu H, Wang H. A novel fault diagnosis based on wavelet transform of IDDT waveform.2004 International Conference on Communications, Circuits and Systems, 2004: 1320-1324.
[93]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.
[94]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.
[95] Song G, Wang H, Liu H , Jiang S. Fault diagnosis of analog circuits based on wavelet neural network. DCABES 2006 Proceedings, 2006: 803-807.
[96] Song G, Wang H, Jiang S, Liu H. Fault diagnosis approach of analog circuits based on genetic wavelet neural network. International Conference on Electronic Measurement and Instruments, 2007: 3-675-3-679.
[97]刘红,陈光礻禹,宋国明, et al.基于AdaBoost集成网络的模拟电路单软故障诊断.仪器仪表学报, 2010, 31(4): 851-856.
[98]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.北京航空航天大学学报, 2003, 29(9):789-792.
[99]罗志勇,史忠科.一种模拟电路的支持向量机故障诊断方法.计算机工程, 2006, 32(15): 34-36.
[100]谢佑川,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):167-171.
[101] Wang A, Liu J, Wu J. Research on soft fault diagnosis algorithm of analogy circuits based on DDAGSVMs. Proceeding of the 2007 IEEE International Conference on Integration Technology, 2007, 3:499-503.
[102]孙永奎,陈光礻禹,李辉.支持向量机在模拟电路故障诊断中的应用.电子测量与仪器学报, 2008, 22(2):72-75.
[103]孙永奎,陈光礻禹,李辉.基于可测性分析和支持向量机的模拟电路故障诊断.仪器仪表学报, 2008, 29(6): 1182-1186.
[104]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.仪器仪表学报, 2008, 29(6): 1216-1220.
[105] Long B, Huang J, Tian S. Least squares support vector machine based analog circuit fault diagnosis using wavelet transform as preprocessor. 2008 International Conference on Communications, Circuits and Systems Proceedings, 2008: 1026 -1029.
[106] 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.
[107] Daubechies I. Ten Lecture Notes on Wavelets. Philadelphia: SIAM, 1992.
[108]孙延奎.小波分析及其应用.北京:机械工业出版社, 2005.
[109]张贤达.现代信号处理(第二版).北京:清华大学出版社, 2002.
[110] Kaminska B, Arabi K,Bell I,et al.Analog and mixed signal benchmark circuits first release. proceedings of International Test Conference,1997:183-190.
[111] Zhang Q H, Benveniste A.Wavelet networks. IEEE Transactions on Neural Networks, 1992, 3(6):889-898.
[112]李弼程,罗建书.小波分析及其应用.北京:电子工业出版社, 2003.
[113] Daubechies I. The wavelet transform,time-frequency localization and signal analysis. IEEE Transaction Information Theory, 1990, 36(5):961~1005.
[114] Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks, 1989 2(7): 183-192.
[115] Michalewiz, Z. Genetic algorithm + data structure = evolution programs (3rd edition), Springer-Verlag, New York, 1996.
[116] Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, MIT Press, 1997, (9): 281-287.
[117] Burges C J C. A tutorial on support vector machines for aattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[118] Bredensteiner E J, Bennett K P. Multicategory classification by support vector machines. Computational Optimization and Applications, 1999, 12(1): 53-79.
[119] Li Z, Tang X. Using support vector machines to enhance the performance of bayesian face recognition. IEEE Transactions on Information Forensics and Security, 2007, 2(2): 174-180.
[120]胡寿松,王源.基于支持向量机的非线性系统故障诊断.控制与决策, 2001, 16(5):617-620
[121] Yang J, Zhang Y, Zhu Y. Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mechanical Systems and Signal Processing, 2007, 21(5): 2012-2024.
[122] Vapnik V N. The Nature of statistical learning theory. Springer-Verlag, New York, 1998.
[123] Keerthi S S, Lin C. Asymptotic behavior of support vector machines with gaussian kernel.Neural Computation, 2003, 15(7):1667~1689.
[124] Lin H, Lin C.A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods [EB/OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvm
[125] Liao H, Mrinal K. Efficient architectures for 1-D and 2-D lifting-based wavelet transforms. IEEE Transaction on Signal Processing, 2004, 52(5): 1315-1326.
[126]段晨东,何正嘉.一种基于提升小波变换的故障特征提取方法及其应用.振动与冲击, 2007, 26(2):10-13.
[127] Guo C, Wang T, Ye J, et al. Retinal images fusion based on lifting wavelet transform. 7th World Congress on Intelligent Control and Automation, 2008:8511-8515.
[128] Liu Y, King N. Weighted adaptive lifting-based wavelet transform for image coding. IEEE Transactions on Image Processing, 2008, 17(4):500-511
[129] Daubechies I, Sweldens W. Factoring wavelet transform into lifting steps. Journal of Fourier Analysis and Application, 1998, 4(3):247-269.
[130] Hsu C-W, Lin C-J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425.
[131] Cheong S, Sang H, Lee S. Support vector machines with binary tree architecture for multi-class classification. Neural Information Processing Letters and Reviews, 2004, 2(3):47-51.
[132] Schwenker F. Hierarchical support vector machines for multi-class pattern recognition. Fourth International Conference on knowledge-Based Intelligent Engineering Systems & Allied Technologies, 2000: 561-565.
[133] Vural V and Dy J. A hierarchical method for multi-class support vector machines. Twenty-First International Conference on Machine Learning, 2004: 831-838.
[134] Fei B and Liu J. Binary tree of SVM: a new fast multiclass training and classification algorithm. IEEE Transactions on Neural Networks, 2006, 17(3):696-704.
[135]马笑潇,黄席樾,柴毅.基于SVM的二叉树多类分类算法及其在故障诊断中的应用.控制与决策, 2003, 18(13):272-276.
[136]唐发明,王仲东,陈绵云.支持向量机多分类算法研究.控制与决策, 2005, 20(7):746-749.
[137]孟媛媛,刘希玉.一种新的基于二叉树的SVM多类分类方法.计算机应用, 2005, 25(11): 2653-2657.
[138] Chen J, Wang C. Combining support vector machines with a pairwise decision tree. IEEE Geoscience and Remote Sensing Letters, 2008, 5(3):409-413.
[139] Ahuja R K, Magnanti T L, Orlin J B. Network flows: theory, algorithm, and applications (English Version) [M]. Beijing: Mechanical Industry Press, 2005: 510-536.
[140]肖俊,庄挺越,吴飞.基于层次细节与最小生成树的三维地形识别与检索.软件学报, 2003, 14(11):1955-1963.
[141]李朝健,李朝鹏,李肯立.基于最小生成树的并行分层聚类算法.微电子学与计算机, 2008, 25(9):196-198.
[142] Manevitz L and Yousef M. One-class SVMs for document classification. The Journal of Machine Learning Research, 2002, vol. 2: 139-154.
[143] Marcellin S, Zighed D, Ritschard G. Evaluating decision trees grown with asymmetric entropies. 17th International Symposium on Methodologies for Intelligent Systems, 2008:56-67.