基于（多）小波（包）、神经网络及优化的模拟电路故障诊断研究
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摘要
模拟电路故障诊断的研究是电路测试领域中极具挑战性的前沿和热点研究课题。由于模拟电路响应的连续性、非线性和元器件参数的容差等固有的特点以及模拟电路故障的多样性复杂性,使得传统的故障诊断理论和方法在实际的的应用诊断中难以达到预期的效果。随着现代化电子技术的飞速发展,网络规模和结构日趋功能化和模块化,研究如何运用现代诊断技术快速、准确、有效地诊断模拟电路故障成为实际工程迫切需要解决的课题,也是模拟电路故障诊断理论和方法走向实际应用的关键步骤。小波理论的出现和发展,神经网络理论和方法的日益成熟,利用小波进行故障信号的分析和处理以及用神经网络来进行故障诊断,已成为热门的研究课题,大量的研究成果表明,它们为模拟电路的故障诊断提供了新的途径。本文以神经网络、小波(包)分析、多小波变换、遗传算法、粒子群算法、信号处理等理论为基础,深入研究了模拟电路的故障特征提取和故障诊断方法,本文的研究工作主要体现在以下几个方面：
(1)阐述了神经网络和小波变换、小波包以及多小波变换理论,对常用的BP神经网络进行了详细的阐述,针对BP算法的不足讨论了几种改进BP算法；对小波理论领域的新兴的研究热点—多小波变换进行了探讨。
(2)在分析和阐述神经网络、(多)小波(包)变换各自理论的基础上研究了两者的结合—(多)小波(包)神经网络在模拟电路故障诊断中的应用；比较深入地研究了紧致型小波神经网络和多小波神经网络的结构、学习算法及逼近性质。
(3)对模拟电路故障诊断的关键步骤—故障特征向量的提取进行了详细的讨论和研究,文中研究了四种用于故障特征向量提取的方法—小波提取、最优小波包提取和主成分分析提取和多小波变换提取,通过诊断实例对四种方法各自的优缺点进行了分析和研究。
(4)对神经网络的参数优化方法进行了研究。基于传统神经网络的模拟电路故障诊断方法普遍存在网络收敛慢、易限于局部最优等缺陷。本文分别研究了将遗传算法、粒子群算法优化神经网络的结构和参数,与传统的普通神经网络相比较,这些方法给出的神经网络的学习既包括网络权值的修正,也包括神经网络其它一些参数的调整。通过各自的诊断实例表明通过优化后的神经网络在故障诊断准确率和诊断速度方面有了进一步的提高。
(5)从信号的高阶累积量角度和数据融合的观点出发,研究了基于信号的峭度、偏度特征提取结合信息融合的模拟电路故障诊断方法,并通过诊断实例验证了该方法的高效性和可行性。
(6)为增强论文的实用性,给出了紧致型小波神经网络、遗传小波神经网络和粒子群小波神经网络进行模拟电路故障诊断的MATLAB仿真源代码程序,已在作者的仿真平台上运行通过。
Study on the fault diagnosis of analog circuits is one of the most challenging hot points in the field of circuit test. Because of the inherent characteristics such as the continuity and non-linearity of responsiveness and tolerance of component parameters in analog circuits as well as the diversity and complexity of analog circuit faults, expected effects are difficult to achieve in practical application of traditional fault diagnosis theory and methodology. With the rapid development of modern electronic technology, the size and structure of network will be functionized and modularized gradually. It becomes a pressing issue in practical engineering and the key step for practical application of the fault diagnosis theory and methodology in analog circuits to study the way to find a fast, accurate and efficient diagnostic method for analog circuit faults by using modern diagnostic technique. With the emergence and development of wavelet theory, the theory and methodology of neural network become more sophisticated. The wavelet is used to analyze and process the fault signal, and the neural network is used to carry out fault diagnosis. Both of them have become the hot subject for research. A large number of studies have indicated that these methods appear as new approaches for the fault diagnosis in analog circuits. Based on the theories of neural network, wavelet (packet) analysis, multi-wavelet transform, genetic algorithm, particle swarm algorithm and signal processing, the paper intensively studies on the fault feature extraction and fault diagnosis methodology. The researches of the Paper are mainly as follows:
(1) Explaining the neural network and wavelet transform and the theory of wavelet packet and multi-wavelet transform; describing the common BP neural networks in detail; discussing several improved BP algorithms in respect of the imperfection of BP algorithm; exploring the emerging research focus in the field of wavelet theory-multi-wavelet transform.
(2) On the basis of analysis and elaboration of theoretical study on neural network and (multi) wavelet (packet) transform, exploring their combination-the application of (multi) wavelet (packet) neural network in the fault diagnosis of analog circuits; study on the compact type of wavelet neural network and multi-wavelet neural network's structure, learning algorithm and approximation properties more profoundly.
(3) Discussing the extraction of fault feature vectors, key step for fault diagnosis of analog circuits, in detail. In the Paper, four methods, i.e. the wavelet extraction, the optimal wavelet packet extraction, the analysis extraction on principal components, the multi-wavelet transform extraction, are explored to extract the feature vectors for faults. Based on examples of diagnosis, the advantages and disadvantages of four methods are analyzed and studied.
(4) Exploring the method for parameter optimization of neural network. Such deficiency as slow convergence for network and limiting in local optimization commonly exists in the traditional diagnosis for analog circuits based on neural network. This paper studies the optimized structures and parameters of neural network by using genetic algorithm and particle swarm algorithm respectively. Compared with research of traditional neural network, study of neural network provided by these methods include not only the correction of network weights but also the adjustment of other parameters in neural network. Respective diagnosis examples show that the optimized neural network has been further improved in respect of the accuracy and speed of fault diagnosis.
(5) Exploring the fault diagnosis methods for analog circuits based on the extraction of signal's kurtosis and skewness and combined with information fusion from the perspective of high-order cumulants and data fusion. In addition, examples are listed to demonstrate the efficiency and feasibility of this method.
(6) To enhance the practicality of the paper, given the MATLAB source code program of analog circuits fault diagnosis by using compact-type neural network, genetic wavelet neural network and particle swarm wavelet neural network, it has been run through on the author's simulation platform.
(1)阐述了神经网络和小波变换、小波包以及多小波变换理论,对常用的BP神经网络进行了详细的阐述,针对BP算法的不足讨论了几种改进BP算法；对小波理论领域的新兴的研究热点—多小波变换进行了探讨。
(2)在分析和阐述神经网络、(多)小波(包)变换各自理论的基础上研究了两者的结合—(多)小波(包)神经网络在模拟电路故障诊断中的应用；比较深入地研究了紧致型小波神经网络和多小波神经网络的结构、学习算法及逼近性质。
(3)对模拟电路故障诊断的关键步骤—故障特征向量的提取进行了详细的讨论和研究,文中研究了四种用于故障特征向量提取的方法—小波提取、最优小波包提取和主成分分析提取和多小波变换提取,通过诊断实例对四种方法各自的优缺点进行了分析和研究。
(4)对神经网络的参数优化方法进行了研究。基于传统神经网络的模拟电路故障诊断方法普遍存在网络收敛慢、易限于局部最优等缺陷。本文分别研究了将遗传算法、粒子群算法优化神经网络的结构和参数,与传统的普通神经网络相比较,这些方法给出的神经网络的学习既包括网络权值的修正,也包括神经网络其它一些参数的调整。通过各自的诊断实例表明通过优化后的神经网络在故障诊断准确率和诊断速度方面有了进一步的提高。
(5)从信号的高阶累积量角度和数据融合的观点出发,研究了基于信号的峭度、偏度特征提取结合信息融合的模拟电路故障诊断方法,并通过诊断实例验证了该方法的高效性和可行性。
(6)为增强论文的实用性,给出了紧致型小波神经网络、遗传小波神经网络和粒子群小波神经网络进行模拟电路故障诊断的MATLAB仿真源代码程序,已在作者的仿真平台上运行通过。
Study on the fault diagnosis of analog circuits is one of the most challenging hot points in the field of circuit test. Because of the inherent characteristics such as the continuity and non-linearity of responsiveness and tolerance of component parameters in analog circuits as well as the diversity and complexity of analog circuit faults, expected effects are difficult to achieve in practical application of traditional fault diagnosis theory and methodology. With the rapid development of modern electronic technology, the size and structure of network will be functionized and modularized gradually. It becomes a pressing issue in practical engineering and the key step for practical application of the fault diagnosis theory and methodology in analog circuits to study the way to find a fast, accurate and efficient diagnostic method for analog circuit faults by using modern diagnostic technique. With the emergence and development of wavelet theory, the theory and methodology of neural network become more sophisticated. The wavelet is used to analyze and process the fault signal, and the neural network is used to carry out fault diagnosis. Both of them have become the hot subject for research. A large number of studies have indicated that these methods appear as new approaches for the fault diagnosis in analog circuits. Based on the theories of neural network, wavelet (packet) analysis, multi-wavelet transform, genetic algorithm, particle swarm algorithm and signal processing, the paper intensively studies on the fault feature extraction and fault diagnosis methodology. The researches of the Paper are mainly as follows:
(1) Explaining the neural network and wavelet transform and the theory of wavelet packet and multi-wavelet transform; describing the common BP neural networks in detail; discussing several improved BP algorithms in respect of the imperfection of BP algorithm; exploring the emerging research focus in the field of wavelet theory-multi-wavelet transform.
(2) On the basis of analysis and elaboration of theoretical study on neural network and (multi) wavelet (packet) transform, exploring their combination-the application of (multi) wavelet (packet) neural network in the fault diagnosis of analog circuits; study on the compact type of wavelet neural network and multi-wavelet neural network's structure, learning algorithm and approximation properties more profoundly.
(3) Discussing the extraction of fault feature vectors, key step for fault diagnosis of analog circuits, in detail. In the Paper, four methods, i.e. the wavelet extraction, the optimal wavelet packet extraction, the analysis extraction on principal components, the multi-wavelet transform extraction, are explored to extract the feature vectors for faults. Based on examples of diagnosis, the advantages and disadvantages of four methods are analyzed and studied.
(4) Exploring the method for parameter optimization of neural network. Such deficiency as slow convergence for network and limiting in local optimization commonly exists in the traditional diagnosis for analog circuits based on neural network. This paper studies the optimized structures and parameters of neural network by using genetic algorithm and particle swarm algorithm respectively. Compared with research of traditional neural network, study of neural network provided by these methods include not only the correction of network weights but also the adjustment of other parameters in neural network. Respective diagnosis examples show that the optimized neural network has been further improved in respect of the accuracy and speed of fault diagnosis.
(5) Exploring the fault diagnosis methods for analog circuits based on the extraction of signal's kurtosis and skewness and combined with information fusion from the perspective of high-order cumulants and data fusion. In addition, examples are listed to demonstrate the efficiency and feasibility of this method.
(6) To enhance the practicality of the paper, given the MATLAB source code program of analog circuits fault diagnosis by using compact-type neural network, genetic wavelet neural network and particle swarm wavelet neural network, it has been run through on the author's simulation platform.
引文
[I]Y.He,Y.Tan,Y.Sun. Wavelet neural network approach for fault diagnosis of analogCircuits.IEE Proc.-Circuits Devices and Systems,2004,151(4):379-384
[2]谭阳红.基于小波和神经网络的大规模模拟电路故障诊断研究：[博士学位论文].长沙：湖南大学,2005,1-2
[3]Tan Yanghong,He Yigang,Wu Jie.Neural network method of fault diagnosis for large-scale analogue circuits.Chinese Journal of electronics,2003,12(1):57-60
[4]谭阳红,何怡刚.模拟电路故障诊断的集团方法.电子测量与仪器学报,2003,17(4)：57-62
[5]刘瑞文.反馈系统设计的公理化方法与模拟电路故障诊断问题.讲演记录,上海复旦大学,中国人民解放军国防科大情报资料室,1998,39-55
[6]Chenglin Yang, Shulin, Tian, Bing Long, et al. Methods of Handling the Tolerance and Test-Point Selection Problem for Analog-Circuit Faulat Diagnosis. IEEE Trans. On Instrmmentation and Measurement,2011,60(1):176-185
[7]Wey C-L, Krishman S. Bulit-in self-test(BIST)structure for analog circuit fault diagnosis with current test data. IEEE Trans. On Instrumentation and Measurement,1992,41(4):535-539
[8]Slamani M, Kaminska B. Analog circuit fault diagnosis based on sensitivity computation an functional testing. Design & Test of Computers, IEEE,, 1992,9(1):30-39
[9]Robotycki A, Zielonko R. Fault diagnosis of analog piecewise linear circuits based on homotopy. IEEE Trans. On Instrumentation and Measurement,2002, 51(4):876-881
[10]Huang J-L, Cheng K-T. Test point selection for analog fault diagnosis of unpowered circuit boards. IEEE Trans. On Circuits and system II:Analog and Digital Signal Processing.2000,47(10):977-987
[II]Aminian F, Aminian M, Collins H W. Analog fault diagnosis of actual circuits using neural networks. IEEE Trans. On Instrumentation and Measurement,2002, 51(3):544-550
[12]Worsman M, Wong M W T, Lee Y S. Enhancing the fault diagnosis of linear analog circiuit steady-state DC testing through the analysis of equivalent faults. IEEE Trans. On Circuits and system Ⅰ:Fundamental Theory and Applications, 2003,50(7):932-936
[13]Lifen Yuan. Yigang He, Jiaoying Huang, et al. A New Neural-Network-Based Fault Diagnosis Approach for Analog Circuits by Using Kurtosis and Entropy as a Preprocessor. IEEE Trans. On Instrumentation and Measurement.2010,59(3): 586-595
[14]Chung Kin Ho Shepherd, P R Eberhardt, F Tenten W. Hierarchical fault diagnosis of analog integrated circuits. IEEE Trans. On Circuits and system Ⅰ:Fundamental Theory and Applications,2001,48(8):921-929
[15]Alippi C, Catelani M, Fort A. Automated selection of test frequencies for fault diagnosis in analog electronic circuits. IEEE Trans. On Instrumentation and Measurement,2005,54(3):1033-1044
[16]He Y, Tan Y. Sun Y. Wavelet neural network approach for fault diagnosis of analog circuits. IEE Proceedings Circuits, Devices and System,2004,151(4):379-384
[17]Cannas B, Fanni A, Montisci A. Algebraic Approach to Ambiguity-Group Determination in Nonlinear Analog Circuits. IEEE Trans. On Circuits and system I:Regular Papers,2010;57(2):438-447
[18]Prasad V C, Babu N S C. Selection of test nodes for analog fault diagnosis in dictionary approach. IEEE Trans. On Instrumentation and Measurement,2000, 49(6):1289-1297
[19]Catelani M, Fort A. Soft fault detection and isolation in analog circuits:some reaults and a comparison between a fuzzy approach and radia basis function networks. IEEE Trans. On Instrumentation and Measurement,2002,51(2):196-202
[20]Grasso F, Luchetta A, Manetti S. et al. A Method for the Automatic Selection of Test Frequencies in Analog Fault Diagnosis. IEEE Trans. On Instrumentation and Measurement,2007,56(6):2322-2329
[21]Tadeusiewicz M, Halgas S, Korzybski M. An algorithm for soft-fault diagnosis of linear and nonlinear circuits. IEEE Trans. On Circuits and system I:Fundamental Theory and Applications,2002,49(11):1648-1653
[22]He Y, Sun Y. Neural network-based L1-norm optimization approach for fault diagnosis of nonlinear circuits with tolerance. IEE Proceedings Circuits, Devices and System,2001,148(4):223-228
[23]Yanghong Tan, Yigang He, Chun Cui, et al. A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms. IEEE Trans. On Instrumentation and Measurement,2008,57(11):2631-2639
[24]Czaja Z. Using a Square-Wave Signal for Fualt Diagnosis of Analog Parts of Mixed-Signal Electronic Embedded System. IEEE Trans. On Instrumentation and Measurement,2008,57(8):1589-1595
[25]彭良玉,禹旺兵.基于小波分析和克隆选择算法的模拟电路故障诊断.电工技术学报,2007,22(6)：12-16
[26]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.电工技术学报.2008,23(1)：24-29
[27]彭敏放,何怡刚,吕敬祥.基于智能信息融合的模拟电路故障定位方法.电工技术学报.2005,20(11)：93-96
[28]彭敏放,何怡刚,王耀南,等.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
[28]彭敏放,何怡刚,王耀南.基于神经网络与证据理论的模拟电路故障诊断.电路与系统学报.2005,10(1)：35-39
[30]尉乃红,杨士元,童诗白.基于BP网络的线性电路故障诊断.计算机学报,1997,,20(4)：360-366
[31]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)：1216-1219
[32]刘红,陈光踽,宋国明,等.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
[33]陈圣俭,洪炳熔,王月芳,等.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
[34]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.湖南大学学报(自然科学版),2004,31(6)：38-59
[35]肖迎群,何怡刚.基于脊波网络的模拟电路故障诊断.电工技术学报,2010,25(6)：155-162
[36]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].长沙：湖南大学,2009,1-16
[37]Berkowitz R.S. Condition for Network-element-value Solvablity. IRE Trans. on Circuit Theory,1962,15(9):25-29
[38]N. Navid and A. A.Willson. A theory and an Algorithm for Analog Circuit Fault Diagnosis. IEEE Trans. on CAS,1979,26(7):440-457
[39]J. W. Bandler, et al. Fault Diagnosis of Analog Circuits. Proceedings of the IEEE, 1985,73(8):1279-1327
[40]J. H. Lee and S. D. Bedrosian. Fault Isolation Algorithm for Analog Electronic Systems using the Fuzzy Concept. IEEE Trans. on CAS,1979,26(7):518-522
[41]S. Freeman. Optimum Fault Isolation by Statistical Inference. IEEE Trans. on CAS,1979,26(7):505-512
[42]W.Hochwald and J.D.Bastian. A DC Approach for Analog Fault Dictionary Determination. IEEE Trans. on CAS,1979,26(7):523-529
[43]R. Decarlo, C. Gordon Tableau. Approach to Ac-multi-frequency Fault Diagnosis. In:Proceeding of IEEE Int. Symp. CAS.1981,270-273
[44]Z. F. Huang, C. S. Lin, R. W. Liu. Topological Condition on Multiple Fault Testability of Analog Circuits. In:Proceedings of IEEE Int. Symp. CAS.1982, 1152-1155
[45]J. W. Bandler. Recent Advances in Fault Location of Analog Networks. In:Proc. IEEE Int. Symp.CAS., Canada:1984,660-663
[46]Z. F. Huang, C. S. Lin, R. W. Liu. Node-fault Diagnosis and a Design of Testability. IEEE Trans. On CAS,1983,30(5):257-265
[47]T. Ozawa, M. Yamada. Conditions for Determining Parameter Values in a Linear Active Network from Node Voltage Measurements. In:Proceeding of IEEE Int. Symp.CAS,1982,274-277
[48]孙义闯.模拟电路故障诊断理论与方法综述.大连海运学院学报,1989,15(4)：68-75
[49]A. E. Salama, et al. A Unified Decomposition Approach for Fault Location in Large Analog Networks. IEEE Trans. on CAS,1984,31(7):609-621
[50]T. Ozawa, M.Yamada. Diagnosability in the Decomposition Approach for Fault Location in Large Analog Networks. IEEE Trans, on CAS,1985,32(4):415-426
[51]罗先觉,邱关源.开关电容网络故障子网络的诊断.西安交通大学学报,1990,25(2)：11-20
[52]Chen Yuqing. Experiment on Fault Location in Large-scale Analog Circuits. IEEE Trans. on Instrumentation and Measurement,1993,42(1):30-34
[53]Timo Sorsa, Heikki N. Koivo, Hannu Koivisto. Neural Network in Process Fault Diagnosis, IEEE Trans. on Systems, Man, and Cybernetics,1991,21(4):815-825.
[54]Robert Spain, Shambhu Upadhyaya. Linear Circuit Fault Diagnosis Using Neuromorphic Analyzers, IEEE Transaction on Circuits and Systems-Ⅱ, Analog and Digital Signal Processing,1997,44(3):188-196.
[55]A.Bernieri, M.D'Apuzzo, L. Somsone, et. al. A Neural Network Approach for Identification and Fault Diagnosis on Dynamic Systems, IEEE Trans. on Instrumentation and Measurement,1994,43(6):867-873.
[56]Arvind Srinivasan, Celal Batur. Hopfield Artificial Neural Network Based Fault Detection and Fault Diagnosis on Dynamic Systems, IEEE Trans. on Neural Networks,1994,5(6):890-899
[57]Hsin-Teng Sheu, Yuen-Haw Chang. Robust Fault Diagnosis For Large-Scale Analog Circuit With Measurement Noises. IEEE Trans.on circuit and system-Ⅰ: Fundamental and applications,1997,44(3):198-209
[58]谢涛.基于神经网络和小波变换的模拟电路故障诊断理论与方法：[硕士学位论文].长沙：湖南大学,2007,2-40
[59]El-Gamal M A. Genetically evolved neural networks for fault classification in analog circuits. Neural Computing & Applications,2002,11(2):112-121
[60]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
[61]Aminian M, Aminiam F. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Transactions on Circuits and systems II:Analog and Digital Signal Processing,2000,47(2):151-156
[62]Li Jimin. Fault diagnosis of electronic circuits based on data fusion technique.Electronic Measurement and Instruments,2007.ICEMI'07.8th International Conference on.Xi'an:2007,647-650
[63]He Wuming,Wang Peiliang.Analog circuit fault diagnosis based on RBF neural network optimized by PSO algorithm.Intelligent Computation Technology and Automation(ICICTA),2010 International Conference on.Changsha:2010,628-631
[64]曹怀信,赵建伟.小波分析发展综述.咸阳师范学院学报,2002,17(6)：5-8
[65]徐丽娜.神经网络控制.哈尔滨：哈尔滨工业大学出版社,1999,18-22
[66]闻新,周露,王丹力.MATLAB神经网络应用设计.北京：科学出版社,2000,132-273
[67]Martin T. Hagan神经网络设计,戴葵(译),北京：机械工业出版社,2002,202-205.
[68]P.J.Werbos."Beyond regression:New tools for prediction and analysis in the behavioral science,"Ph.D.Thesis, Harvard University, Cambridge, MA, 1974,16-25
[69]D. E. Rumelhart, G.. E. Hinton and R. J. Williams.Learning representations by back-propagating errors. Nature,1986,323(5),533-536,
[70]D. B. Parker. Learning-logic:Casting the cortex of the human brain in silicon. Technical Report TR-47, Center for Computational Research in Economics and Management Science, MIT, Cambridge, MA,1985,86-92
[71]Y. Le Cun. Une procedure d'apprentissage pour reseau a seuil assymetrique. Cognitiva.1985,85(6),599-604
[72]D. E. Rumelhart and J. L. McClelland, eds. Parallel Distributed Processing: Explorations in the Micro structure of Cognition, vol.1, Cambridge, MA: MITPress,1986,62-75
[73]周华东,叶银忠.现代故障诊断与容错控制.北京：清华大学出版社,2000,50-70
[74]Ashouri M R, Fault detection of analog circuits using neural networks and montecarlo analysis. IEEE trans. CAS,2001,65(10):700-704
[75]Bernieri A, A neural network approach to instrument fault detection and isolation. IEEE trans. CAS,1995,22(11):747-750
[76]Scully J K, Neural Recognition of Diagnostic Test Data Transforms. IEEE trans. CAS,1994,18(7):433-437
[77]Rutkowski J, A two stage neural network DC fault dictionary. IEEE trans. CAS, 1995,20(6):299-302
[78]Catelani M, Application of radial basis function network to the preventive maintenance of electronic analog circuits. IEEE trans.1999,12(3):510-513
[79]程正兴,杨守志,张玲玲.多小波理论的发展与研究.工程数学学报,,2001,18(5)：1-16
[80]Chui C K.Lian J. A Study of orthogonormal multiwavelet.J Approx Numer Math,1996,20(8):273-298
[81]Lawton W Lee SL,Shen Z.Analgorithm for matrix extension and wavelet construction.Math Comput,1996,65(10):723-737
[82]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
[83]王承,谢永乐.小波神经网络在模拟电路故障诊断中的应用.系统仿真学报,2005,17(8)：1937-1938.
[84]金瑜.基于小波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,2008,56-57
[85]韩宝如.模拟电路故障诊断的小波与模糊神经网络方法研究：[硕士学位论文].秦皇岛：燕山大学,2007,35-39
[86]Jiao L C,Pan J,Fang Y W.Multiwavelet neural network and its approximation properties.IEEE Transactions on Neural Networks,2001,12(5):1060-1066
[87]王军锋,冯象初,宋国乡.多分辨正交多小波网络的结构与算法研究.系统工程与电子技术.2003,25(11)：1411-1413
[88]虞和济,陈长征.基于神经网络的智能诊断.北京：冶金工业出版社,2000,16-40
[89]Holland J H.Adaptation in Natural and Artificial System.Cambridge:MIT Press.1975.10-42
[90]傅荟璇,赵红MATLAB神经网络应用设计.北京：机械工业出版社2010,153-163
[91]田雨波.混合神经网络技术.北京：科学出版社,2009,125-176
[92]Kitano H.Designing neural network using genetic algorithm with graph generation system.Complex System,1990,4(4):461-476
[93]Vaario J,Ohsuga S.Adaptive neural architectures through growth control.New York:ASM Press,1991,11-16
[94]Mjolsness E, Sharp D H, Alpert B K. Scaling.machine learning,and genetic neural nets. Advances in Applied Mathematics,1989,10(2):137-163
[95]Whitely D, Starkweather T, Bogart C. Genetic algorithms and neural networks:optimizing connections and connectivity. Parallel Computation, 1990,14(3):347-361
[96]Belew R K, McInerney J, Schraudolph N. Evolving networks:Using the genetic algorithms with connectionist learning. Proceeding Second Artificial Life Conference,1991,511-547
[97]Caudel T P, Dolan C P. Parametric connectivity:Trainning of constrained networks using genetic algorithms. Proceeding of 3 rd Conference on GA,Arlington,1989,368-371
[98]Kennedy J, Eberhart R C. Particle swarm optimization. IEEE International Conference on Neural Networks.1995,1942-1948
[99]Clerc M. Particle Swarm Optimization. London:ISTE Publishing Company,2006,32-45
[100]曾建潮,介婧,崔志华.粒子群算法.北京：科学出版社,2004,22-48
[101]Poli R. Analysis of the publications on the applications of particle swarm optimization. Journal of Artificial Evolution and Applicaions,2008,8(2):4-10
[102]Robinson J, Rahmat-Samii Y. Particle swarm optimization in electromagnetics. IEEE Trans.on Antennas and Propagation,2004,52(2):397-407
[103]张丽平.粒子群算法的理论与实践：[博士学位论文].杭州：浙江大学,2005,12-35
[104]Zheng Y L, Ma L H, Zhang L Y, et al. On the convergence analysis and parameter selection in particle swarm optimization. Proceedings of the Second International Conference on Machine Learning and Cybernetics,2003,1802-1807
[105]Lic C, Feng Q Y. The standard particle swarm optimization algorithm convergence analysis and parameter selection. Proceedings of Third International Conference on Natural Computation,2007,427-430
[106]Clerc M, Kennedy J. The particle swarm:Explosion stability and convergence in a multi-dimensional complex space.IEEE Trans. On Evolutionary Computation, 2002,6(1):58-73
[107]高尚,杨静宇.群智能算法及其应用.北京：中国水利水电出版社,2006,66-82
[108]Shi Y, Eberhart R C. A modified particle swarm optimize. Proceedings of the IEEE Conference on Evolutionary Computation,1998,69-73
[109]田雨波,朱人杰,薛权祥.粒子群优化算法中惯性权重的研究进展.计算机工程与应用,2008,44(23)：39-41
[110]Shi Y, Eberhart R C. Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation,1999,1945-1950
[111]Zheng Y L, Ma L H, Zhang L Y, et al. On the convergence analysis and parameter selection in particle swarm optimization. Proceedings of the Second International Conference on Machine Learning and Cybernetics. Xi'an,2003,1802-1807
[112]崔红梅,朱庆保.粒子群算法的参数选择及收敛性分析.计算机工程与应用,2007,43(23)：89-91
[113]王丽,王晓凯.一种非线性改变惯性权重的粒子群优化算法.计算机工程与应用,2007,43(4)：47-48
[114]Chatterjee A, Siarry P. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers and Operations Research,2006,33(3): 859-871
[115]王启付,王战江,王书亭.一种动态改变惯性权重的粒子群优化算法.中国机械工程,2005,16(11)：945-948
[116]刘建华,樊晓平,瞿志华.一种惯性权重动态调整的新型粒子群优化算法.计算机工程与应用,2007,43(7)：68-70
[117]韩江洪,李正荣,魏振春.一种自适应粒子群优化算法及其仿真研究.系统仿真学报,2006,18(10)：2969-2971
[118]延丽平,曾建潮.具有自适应随机惯性权重的粒子群优化算法.计算机工程与设计,2006,27(4)：4677-4679
[119]Clerc M. The swarm and queen:Towards a deterministic and adaptive particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation,1999,1951-1957
[120]Eberhart R, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. IEEE Congress on Evolutionary Computation,2000,84-88
[121]吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报,2004,33(3)：416-419
[122]Stacey A, Jancic M, Grund Y I. Particle swarm optimization with mutation. The 2003 Congress on Evolutionary Computation,Canbella,2003,1425-1430
[123]Berghf V D, Engelbrecht A P. Cooperative learning in neural networks using particle swarm optimizers. South African Computer Journal,2000,26(11):84-90
[124]高海兵,高亮,周驰,等.基于粒子群优化的神经网络训练算法研究.电子学报,2004,32(9)：1572-1574
[125]Carvalho M, Ludermir T R. Particle swarm optimization of neural network architectures and weights.7th International Conference on Hybrid Intelligent System 2007,Kaiserslautern;2007,336-339
[126]唐贤伦,庄陵,李银国,等.混合粒子群优化算法优化前向神经网络结构和参数.计算机应用研究,2007,24(12)：91-93
[127]田雨波,李正强,王建华.矩形微带天线谐振频率的粒子群神经网络建模.微波学报,2009,25(5)：45-50
[128]Xiang C C, Huang X Y, Huang D R, et al. Wavelets neural network based on particle swarm optimization algorithm for fault diagnosis. Proceedings of the First International Conference on Innovative Computing, Information and Control, Beijing,2006,320-323
[129]Zhang J R, Zhang J, Lok T M, et al. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation,2007,1026-1037
[130]Chen G C, Yu J S. Particle swarm optimization neural network and its application in soft-sensing modeling. Lecture Notes in Computer Science,2005,36(11): 320-323
[131]Ribeiro P F, Schlansker W K. A hybrid particle swarm and neural network approach for reactive power control, http://www.calvin.edu/-pribeiro/courses/ engr302/Samples/ReactivePower-PSO-wks.pdf,2006-10-6
[132]Guerra F A, Coelho L. Radial basis neural network learning based on particle swarm optimization to multistep prediction of chaotic Lorenz system. Fifth International Conference on Hybrid Intelligent System,2005,521-523
[133]Ma M, Zhang L B, Ma J, et al. Fuzzy neural network optimization by a particle swarm optimization algorithm. Lecture Notes in Computer Science,2006,39(6): 752-761
[134]段晓东,高红橄,张学东,等.粒子群算法种群结构与种群多样性的关系研究.计算机科学,2007,34(11)：164-166
[135]黄润生,黄浩.混沌及其应用.武汉：武汉大学出版社,2005,20-42
[136]阎平凡,张长水.人工神经网络与模拟进化计算.北京：清华大学出版社,2000,165-166
[137]Aapo Hyvarinen. Juha Karhunen,Erkki Oja. Independent Component Analysis.周宗潭,董国华,徐昕,等译.北京：电子工业出版社,2007,26-27
[138]Shuli Sun. Optimal and self-tuning information fusion Kalman multi-step predictor. Aerospace and Electronic Systems,2007,43(2):418-427.
[139]Shivappa, S.T. Trivedi. M.M. Rao, B.D. Audiovisual Information Fusion in Human-Computer Interfaces and Intelligent Environments:A Survey. Proceedings of the IEEE,2010,98(10):1692-1715.
[140]Yongmian Zhang, Qiang Ji. Efficient Sensor Selection for Active Information Fusion. Systems, Man, and Cybernetics, Part B:Cybernetics,2010,40(3):719-728.
[141]王承,孙秀斌,谢永乐,基于神经网络信息融合技术的模拟电路故障诊断.电路与系统学报,2007,12(5)：25-29
[142]Bishop C. M. Neural Networks for Pattern Recognition. Oxford:Oxford University Press.1995,15-63.
[143]Meghana R, Ransing. Issues in Learning Cause and Effect Relationships from Examples:With particularly emphasis on casting process.Swansea:University of Wales Swansea,2002,70-78
[144]N. M. Nawi, R. S. Ransing, M. R. Ransing. A new method to improve the gradient based search direction to enhance the computational efficiency of back propagation based Neural Network algorithms. Second Asia International Conference on Modelling & Simulation, Kuala Lumpur, Malaysia,2008,549-551.
[145]T Deliyannis, Y Sun, J K Fidler Continuous-Time Active Filter Design Boca Raton, FL:CRC Press,1999,26-30
[2]谭阳红.基于小波和神经网络的大规模模拟电路故障诊断研究：[博士学位论文].长沙：湖南大学,2005,1-2
[3]Tan Yanghong,He Yigang,Wu Jie.Neural network method of fault diagnosis for large-scale analogue circuits.Chinese Journal of electronics,2003,12(1):57-60
[4]谭阳红,何怡刚.模拟电路故障诊断的集团方法.电子测量与仪器学报,2003,17(4)：57-62
[5]刘瑞文.反馈系统设计的公理化方法与模拟电路故障诊断问题.讲演记录,上海复旦大学,中国人民解放军国防科大情报资料室,1998,39-55
[6]Chenglin Yang, Shulin, Tian, Bing Long, et al. Methods of Handling the Tolerance and Test-Point Selection Problem for Analog-Circuit Faulat Diagnosis. IEEE Trans. On Instrmmentation and Measurement,2011,60(1):176-185
[7]Wey C-L, Krishman S. Bulit-in self-test(BIST)structure for analog circuit fault diagnosis with current test data. IEEE Trans. On Instrumentation and Measurement,1992,41(4):535-539
[8]Slamani M, Kaminska B. Analog circuit fault diagnosis based on sensitivity computation an functional testing. Design & Test of Computers, IEEE,, 1992,9(1):30-39
[9]Robotycki A, Zielonko R. Fault diagnosis of analog piecewise linear circuits based on homotopy. IEEE Trans. On Instrumentation and Measurement,2002, 51(4):876-881
[10]Huang J-L, Cheng K-T. Test point selection for analog fault diagnosis of unpowered circuit boards. IEEE Trans. On Circuits and system II:Analog and Digital Signal Processing.2000,47(10):977-987
[II]Aminian F, Aminian M, Collins H W. Analog fault diagnosis of actual circuits using neural networks. IEEE Trans. On Instrumentation and Measurement,2002, 51(3):544-550
[12]Worsman M, Wong M W T, Lee Y S. Enhancing the fault diagnosis of linear analog circiuit steady-state DC testing through the analysis of equivalent faults. IEEE Trans. On Circuits and system Ⅰ:Fundamental Theory and Applications, 2003,50(7):932-936
[13]Lifen Yuan. Yigang He, Jiaoying Huang, et al. A New Neural-Network-Based Fault Diagnosis Approach for Analog Circuits by Using Kurtosis and Entropy as a Preprocessor. IEEE Trans. On Instrumentation and Measurement.2010,59(3): 586-595
[14]Chung Kin Ho Shepherd, P R Eberhardt, F Tenten W. Hierarchical fault diagnosis of analog integrated circuits. IEEE Trans. On Circuits and system Ⅰ:Fundamental Theory and Applications,2001,48(8):921-929
[15]Alippi C, Catelani M, Fort A. Automated selection of test frequencies for fault diagnosis in analog electronic circuits. IEEE Trans. On Instrumentation and Measurement,2005,54(3):1033-1044
[16]He Y, Tan Y. Sun Y. Wavelet neural network approach for fault diagnosis of analog circuits. IEE Proceedings Circuits, Devices and System,2004,151(4):379-384
[17]Cannas B, Fanni A, Montisci A. Algebraic Approach to Ambiguity-Group Determination in Nonlinear Analog Circuits. IEEE Trans. On Circuits and system I:Regular Papers,2010;57(2):438-447
[18]Prasad V C, Babu N S C. Selection of test nodes for analog fault diagnosis in dictionary approach. IEEE Trans. On Instrumentation and Measurement,2000, 49(6):1289-1297
[19]Catelani M, Fort A. Soft fault detection and isolation in analog circuits:some reaults and a comparison between a fuzzy approach and radia basis function networks. IEEE Trans. On Instrumentation and Measurement,2002,51(2):196-202
[20]Grasso F, Luchetta A, Manetti S. et al. A Method for the Automatic Selection of Test Frequencies in Analog Fault Diagnosis. IEEE Trans. On Instrumentation and Measurement,2007,56(6):2322-2329
[21]Tadeusiewicz M, Halgas S, Korzybski M. An algorithm for soft-fault diagnosis of linear and nonlinear circuits. IEEE Trans. On Circuits and system I:Fundamental Theory and Applications,2002,49(11):1648-1653
[22]He Y, Sun Y. Neural network-based L1-norm optimization approach for fault diagnosis of nonlinear circuits with tolerance. IEE Proceedings Circuits, Devices and System,2001,148(4):223-228
[23]Yanghong Tan, Yigang He, Chun Cui, et al. A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms. IEEE Trans. On Instrumentation and Measurement,2008,57(11):2631-2639
[24]Czaja Z. Using a Square-Wave Signal for Fualt Diagnosis of Analog Parts of Mixed-Signal Electronic Embedded System. IEEE Trans. On Instrumentation and Measurement,2008,57(8):1589-1595
[25]彭良玉,禹旺兵.基于小波分析和克隆选择算法的模拟电路故障诊断.电工技术学报,2007,22(6)：12-16
[26]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.电工技术学报.2008,23(1)：24-29
[27]彭敏放,何怡刚,吕敬祥.基于智能信息融合的模拟电路故障定位方法.电工技术学报.2005,20(11)：93-96
[28]彭敏放,何怡刚,王耀南,等.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
[28]彭敏放,何怡刚,王耀南.基于神经网络与证据理论的模拟电路故障诊断.电路与系统学报.2005,10(1)：35-39
[30]尉乃红,杨士元,童诗白.基于BP网络的线性电路故障诊断.计算机学报,1997,,20(4)：360-366
[31]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)：1216-1219
[32]刘红,陈光踽,宋国明,等.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
[33]陈圣俭,洪炳熔,王月芳,等.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
[34]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.湖南大学学报(自然科学版),2004,31(6)：38-59
[35]肖迎群,何怡刚.基于脊波网络的模拟电路故障诊断.电工技术学报,2010,25(6)：155-162
[36]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].长沙：湖南大学,2009,1-16
[37]Berkowitz R.S. Condition for Network-element-value Solvablity. IRE Trans. on Circuit Theory,1962,15(9):25-29
[38]N. Navid and A. A.Willson. A theory and an Algorithm for Analog Circuit Fault Diagnosis. IEEE Trans. on CAS,1979,26(7):440-457
[39]J. W. Bandler, et al. Fault Diagnosis of Analog Circuits. Proceedings of the IEEE, 1985,73(8):1279-1327
[40]J. H. Lee and S. D. Bedrosian. Fault Isolation Algorithm for Analog Electronic Systems using the Fuzzy Concept. IEEE Trans. on CAS,1979,26(7):518-522
[41]S. Freeman. Optimum Fault Isolation by Statistical Inference. IEEE Trans. on CAS,1979,26(7):505-512
[42]W.Hochwald and J.D.Bastian. A DC Approach for Analog Fault Dictionary Determination. IEEE Trans. on CAS,1979,26(7):523-529
[43]R. Decarlo, C. Gordon Tableau. Approach to Ac-multi-frequency Fault Diagnosis. In:Proceeding of IEEE Int. Symp. CAS.1981,270-273
[44]Z. F. Huang, C. S. Lin, R. W. Liu. Topological Condition on Multiple Fault Testability of Analog Circuits. In:Proceedings of IEEE Int. Symp. CAS.1982, 1152-1155
[45]J. W. Bandler. Recent Advances in Fault Location of Analog Networks. In:Proc. IEEE Int. Symp.CAS., Canada:1984,660-663
[46]Z. F. Huang, C. S. Lin, R. W. Liu. Node-fault Diagnosis and a Design of Testability. IEEE Trans. On CAS,1983,30(5):257-265
[47]T. Ozawa, M. Yamada. Conditions for Determining Parameter Values in a Linear Active Network from Node Voltage Measurements. In:Proceeding of IEEE Int. Symp.CAS,1982,274-277
[48]孙义闯.模拟电路故障诊断理论与方法综述.大连海运学院学报,1989,15(4)：68-75
[49]A. E. Salama, et al. A Unified Decomposition Approach for Fault Location in Large Analog Networks. IEEE Trans. on CAS,1984,31(7):609-621
[50]T. Ozawa, M.Yamada. Diagnosability in the Decomposition Approach for Fault Location in Large Analog Networks. IEEE Trans, on CAS,1985,32(4):415-426
[51]罗先觉,邱关源.开关电容网络故障子网络的诊断.西安交通大学学报,1990,25(2)：11-20
[52]Chen Yuqing. Experiment on Fault Location in Large-scale Analog Circuits. IEEE Trans. on Instrumentation and Measurement,1993,42(1):30-34
[53]Timo Sorsa, Heikki N. Koivo, Hannu Koivisto. Neural Network in Process Fault Diagnosis, IEEE Trans. on Systems, Man, and Cybernetics,1991,21(4):815-825.
[54]Robert Spain, Shambhu Upadhyaya. Linear Circuit Fault Diagnosis Using Neuromorphic Analyzers, IEEE Transaction on Circuits and Systems-Ⅱ, Analog and Digital Signal Processing,1997,44(3):188-196.
[55]A.Bernieri, M.D'Apuzzo, L. Somsone, et. al. A Neural Network Approach for Identification and Fault Diagnosis on Dynamic Systems, IEEE Trans. on Instrumentation and Measurement,1994,43(6):867-873.
[56]Arvind Srinivasan, Celal Batur. Hopfield Artificial Neural Network Based Fault Detection and Fault Diagnosis on Dynamic Systems, IEEE Trans. on Neural Networks,1994,5(6):890-899
[57]Hsin-Teng Sheu, Yuen-Haw Chang. Robust Fault Diagnosis For Large-Scale Analog Circuit With Measurement Noises. IEEE Trans.on circuit and system-Ⅰ: Fundamental and applications,1997,44(3):198-209
[58]谢涛.基于神经网络和小波变换的模拟电路故障诊断理论与方法：[硕士学位论文].长沙：湖南大学,2007,2-40
[59]El-Gamal M A. Genetically evolved neural networks for fault classification in analog circuits. Neural Computing & Applications,2002,11(2):112-121
[60]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
[61]Aminian M, Aminiam F. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Transactions on Circuits and systems II:Analog and Digital Signal Processing,2000,47(2):151-156
[62]Li Jimin. Fault diagnosis of electronic circuits based on data fusion technique.Electronic Measurement and Instruments,2007.ICEMI'07.8th International Conference on.Xi'an:2007,647-650
[63]He Wuming,Wang Peiliang.Analog circuit fault diagnosis based on RBF neural network optimized by PSO algorithm.Intelligent Computation Technology and Automation(ICICTA),2010 International Conference on.Changsha:2010,628-631
[64]曹怀信,赵建伟.小波分析发展综述.咸阳师范学院学报,2002,17(6)：5-8
[65]徐丽娜.神经网络控制.哈尔滨：哈尔滨工业大学出版社,1999,18-22
[66]闻新,周露,王丹力.MATLAB神经网络应用设计.北京：科学出版社,2000,132-273
[67]Martin T. Hagan神经网络设计,戴葵(译),北京：机械工业出版社,2002,202-205.
[68]P.J.Werbos."Beyond regression:New tools for prediction and analysis in the behavioral science,"Ph.D.Thesis, Harvard University, Cambridge, MA, 1974,16-25
[69]D. E. Rumelhart, G.. E. Hinton and R. J. Williams.Learning representations by back-propagating errors. Nature,1986,323(5),533-536,
[70]D. B. Parker. Learning-logic:Casting the cortex of the human brain in silicon. Technical Report TR-47, Center for Computational Research in Economics and Management Science, MIT, Cambridge, MA,1985,86-92
[71]Y. Le Cun. Une procedure d'apprentissage pour reseau a seuil assymetrique. Cognitiva.1985,85(6),599-604
[72]D. E. Rumelhart and J. L. McClelland, eds. Parallel Distributed Processing: Explorations in the Micro structure of Cognition, vol.1, Cambridge, MA: MITPress,1986,62-75
[73]周华东,叶银忠.现代故障诊断与容错控制.北京：清华大学出版社,2000,50-70
[74]Ashouri M R, Fault detection of analog circuits using neural networks and montecarlo analysis. IEEE trans. CAS,2001,65(10):700-704
[75]Bernieri A, A neural network approach to instrument fault detection and isolation. IEEE trans. CAS,1995,22(11):747-750
[76]Scully J K, Neural Recognition of Diagnostic Test Data Transforms. IEEE trans. CAS,1994,18(7):433-437
[77]Rutkowski J, A two stage neural network DC fault dictionary. IEEE trans. CAS, 1995,20(6):299-302
[78]Catelani M, Application of radial basis function network to the preventive maintenance of electronic analog circuits. IEEE trans.1999,12(3):510-513
[79]程正兴,杨守志,张玲玲.多小波理论的发展与研究.工程数学学报,,2001,18(5)：1-16
[80]Chui C K.Lian J. A Study of orthogonormal multiwavelet.J Approx Numer Math,1996,20(8):273-298
[81]Lawton W Lee SL,Shen Z.Analgorithm for matrix extension and wavelet construction.Math Comput,1996,65(10):723-737
[82]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
[83]王承,谢永乐.小波神经网络在模拟电路故障诊断中的应用.系统仿真学报,2005,17(8)：1937-1938.
[84]金瑜.基于小波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,2008,56-57
[85]韩宝如.模拟电路故障诊断的小波与模糊神经网络方法研究：[硕士学位论文].秦皇岛：燕山大学,2007,35-39
[86]Jiao L C,Pan J,Fang Y W.Multiwavelet neural network and its approximation properties.IEEE Transactions on Neural Networks,2001,12(5):1060-1066
[87]王军锋,冯象初,宋国乡.多分辨正交多小波网络的结构与算法研究.系统工程与电子技术.2003,25(11)：1411-1413
[88]虞和济,陈长征.基于神经网络的智能诊断.北京：冶金工业出版社,2000,16-40
[89]Holland J H.Adaptation in Natural and Artificial System.Cambridge:MIT Press.1975.10-42
[90]傅荟璇,赵红MATLAB神经网络应用设计.北京：机械工业出版社2010,153-163
[91]田雨波.混合神经网络技术.北京：科学出版社,2009,125-176
[92]Kitano H.Designing neural network using genetic algorithm with graph generation system.Complex System,1990,4(4):461-476
[93]Vaario J,Ohsuga S.Adaptive neural architectures through growth control.New York:ASM Press,1991,11-16
[94]Mjolsness E, Sharp D H, Alpert B K. Scaling.machine learning,and genetic neural nets. Advances in Applied Mathematics,1989,10(2):137-163
[95]Whitely D, Starkweather T, Bogart C. Genetic algorithms and neural networks:optimizing connections and connectivity. Parallel Computation, 1990,14(3):347-361
[96]Belew R K, McInerney J, Schraudolph N. Evolving networks:Using the genetic algorithms with connectionist learning. Proceeding Second Artificial Life Conference,1991,511-547
[97]Caudel T P, Dolan C P. Parametric connectivity:Trainning of constrained networks using genetic algorithms. Proceeding of 3 rd Conference on GA,Arlington,1989,368-371
[98]Kennedy J, Eberhart R C. Particle swarm optimization. IEEE International Conference on Neural Networks.1995,1942-1948
[99]Clerc M. Particle Swarm Optimization. London:ISTE Publishing Company,2006,32-45
[100]曾建潮,介婧,崔志华.粒子群算法.北京：科学出版社,2004,22-48
[101]Poli R. Analysis of the publications on the applications of particle swarm optimization. Journal of Artificial Evolution and Applicaions,2008,8(2):4-10
[102]Robinson J, Rahmat-Samii Y. Particle swarm optimization in electromagnetics. IEEE Trans.on Antennas and Propagation,2004,52(2):397-407
[103]张丽平.粒子群算法的理论与实践：[博士学位论文].杭州：浙江大学,2005,12-35
[104]Zheng Y L, Ma L H, Zhang L Y, et al. On the convergence analysis and parameter selection in particle swarm optimization. Proceedings of the Second International Conference on Machine Learning and Cybernetics,2003,1802-1807
[105]Lic C, Feng Q Y. The standard particle swarm optimization algorithm convergence analysis and parameter selection. Proceedings of Third International Conference on Natural Computation,2007,427-430
[106]Clerc M, Kennedy J. The particle swarm:Explosion stability and convergence in a multi-dimensional complex space.IEEE Trans. On Evolutionary Computation, 2002,6(1):58-73
[107]高尚,杨静宇.群智能算法及其应用.北京：中国水利水电出版社,2006,66-82
[108]Shi Y, Eberhart R C. A modified particle swarm optimize. Proceedings of the IEEE Conference on Evolutionary Computation,1998,69-73
[109]田雨波,朱人杰,薛权祥.粒子群优化算法中惯性权重的研究进展.计算机工程与应用,2008,44(23)：39-41
[110]Shi Y, Eberhart R C. Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation,1999,1945-1950
[111]Zheng Y L, Ma L H, Zhang L Y, et al. On the convergence analysis and parameter selection in particle swarm optimization. Proceedings of the Second International Conference on Machine Learning and Cybernetics. Xi'an,2003,1802-1807
[112]崔红梅,朱庆保.粒子群算法的参数选择及收敛性分析.计算机工程与应用,2007,43(23)：89-91
[113]王丽,王晓凯.一种非线性改变惯性权重的粒子群优化算法.计算机工程与应用,2007,43(4)：47-48
[114]Chatterjee A, Siarry P. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers and Operations Research,2006,33(3): 859-871
[115]王启付,王战江,王书亭.一种动态改变惯性权重的粒子群优化算法.中国机械工程,2005,16(11)：945-948
[116]刘建华,樊晓平,瞿志华.一种惯性权重动态调整的新型粒子群优化算法.计算机工程与应用,2007,43(7)：68-70
[117]韩江洪,李正荣,魏振春.一种自适应粒子群优化算法及其仿真研究.系统仿真学报,2006,18(10)：2969-2971
[118]延丽平,曾建潮.具有自适应随机惯性权重的粒子群优化算法.计算机工程与设计,2006,27(4)：4677-4679
[119]Clerc M. The swarm and queen:Towards a deterministic and adaptive particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation,1999,1951-1957
[120]Eberhart R, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. IEEE Congress on Evolutionary Computation,2000,84-88
[121]吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报,2004,33(3)：416-419
[122]Stacey A, Jancic M, Grund Y I. Particle swarm optimization with mutation. The 2003 Congress on Evolutionary Computation,Canbella,2003,1425-1430
[123]Berghf V D, Engelbrecht A P. Cooperative learning in neural networks using particle swarm optimizers. South African Computer Journal,2000,26(11):84-90
[124]高海兵,高亮,周驰,等.基于粒子群优化的神经网络训练算法研究.电子学报,2004,32(9)：1572-1574
[125]Carvalho M, Ludermir T R. Particle swarm optimization of neural network architectures and weights.7th International Conference on Hybrid Intelligent System 2007,Kaiserslautern;2007,336-339
[126]唐贤伦,庄陵,李银国,等.混合粒子群优化算法优化前向神经网络结构和参数.计算机应用研究,2007,24(12)：91-93
[127]田雨波,李正强,王建华.矩形微带天线谐振频率的粒子群神经网络建模.微波学报,2009,25(5)：45-50
[128]Xiang C C, Huang X Y, Huang D R, et al. Wavelets neural network based on particle swarm optimization algorithm for fault diagnosis. Proceedings of the First International Conference on Innovative Computing, Information and Control, Beijing,2006,320-323
[129]Zhang J R, Zhang J, Lok T M, et al. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation,2007,1026-1037
[130]Chen G C, Yu J S. Particle swarm optimization neural network and its application in soft-sensing modeling. Lecture Notes in Computer Science,2005,36(11): 320-323
[131]Ribeiro P F, Schlansker W K. A hybrid particle swarm and neural network approach for reactive power control, http://www.calvin.edu/-pribeiro/courses/ engr302/Samples/ReactivePower-PSO-wks.pdf,2006-10-6
[132]Guerra F A, Coelho L. Radial basis neural network learning based on particle swarm optimization to multistep prediction of chaotic Lorenz system. Fifth International Conference on Hybrid Intelligent System,2005,521-523
[133]Ma M, Zhang L B, Ma J, et al. Fuzzy neural network optimization by a particle swarm optimization algorithm. Lecture Notes in Computer Science,2006,39(6): 752-761
[134]段晓东,高红橄,张学东,等.粒子群算法种群结构与种群多样性的关系研究.计算机科学,2007,34(11)：164-166
[135]黄润生,黄浩.混沌及其应用.武汉：武汉大学出版社,2005,20-42
[136]阎平凡,张长水.人工神经网络与模拟进化计算.北京：清华大学出版社,2000,165-166
[137]Aapo Hyvarinen. Juha Karhunen,Erkki Oja. Independent Component Analysis.周宗潭,董国华,徐昕,等译.北京：电子工业出版社,2007,26-27
[138]Shuli Sun. Optimal and self-tuning information fusion Kalman multi-step predictor. Aerospace and Electronic Systems,2007,43(2):418-427.
[139]Shivappa, S.T. Trivedi. M.M. Rao, B.D. Audiovisual Information Fusion in Human-Computer Interfaces and Intelligent Environments:A Survey. Proceedings of the IEEE,2010,98(10):1692-1715.
[140]Yongmian Zhang, Qiang Ji. Efficient Sensor Selection for Active Information Fusion. Systems, Man, and Cybernetics, Part B:Cybernetics,2010,40(3):719-728.
[141]王承,孙秀斌,谢永乐,基于神经网络信息融合技术的模拟电路故障诊断.电路与系统学报,2007,12(5)：25-29
[142]Bishop C. M. Neural Networks for Pattern Recognition. Oxford:Oxford University Press.1995,15-63.
[143]Meghana R, Ransing. Issues in Learning Cause and Effect Relationships from Examples:With particularly emphasis on casting process.Swansea:University of Wales Swansea,2002,70-78
[144]N. M. Nawi, R. S. Ransing, M. R. Ransing. A new method to improve the gradient based search direction to enhance the computational efficiency of back propagation based Neural Network algorithms. Second Asia International Conference on Modelling & Simulation, Kuala Lumpur, Malaysia,2008,549-551.
[145]T Deliyannis, Y Sun, J K Fidler Continuous-Time Active Filter Design Boca Raton, FL:CRC Press,1999,26-30