动车组制动控制系统故障诊断方法研究
摘要
随着高速铁路在我国的普及,动车组的运行安全问题受到越来越多的关注。如何保障列车安全可靠的运行,成为近期的研究热点和难点问题。
制动控制系统作为动车组制动系统的关键组成部分,能否正常稳定工作,直接影响动车组的安全可靠运行,因此对制动控制系统的状态监测和故障诊断显得尤为重要和关键。由于动车组制动控制系统的复杂性及引进消化吸收的时间不长,制动控制系统故障仍较为多发,严重影响着动车组的正常稳定可靠运行。因此本课题对动车组制动控制系统中关键设备和部件的故障及潜在故障隐患开展深入研究,提出和改进了已有的故障特征提取技术和故障诊断方法,用于动车组制动控制系统关键设备和部件的故障诊断,以提高制动控制系统的可靠性、稳定性和主动安全防护能力。设计开发了制动控制单元自动化测试与故障诊断系统,并运用在CRH2型动车组制动控制系统的监测与故障诊断中,取得了很好的效果。主要研究内容和取得的创新性研究成果主要有以下几个方面：
针对动车组制动控制系统的关键设备-制动控制单元(制动控制器)模拟电路软故障在复杂电磁环境下的故障特征提取问题,本文提出了一种基于形态学滤波-小波包能量熵的故障特征提取新方法。首先采用组合式形态学滤波器对故障信号进行预处理,消除白噪声、尖峰脉冲及未知噪声的干扰；然后采用小波包分析方法对预处理后的故障信号进行多层小波包分解,提取小波包能量熵组成故障特征向量输入到最小二乘支持向量机中进行分类识别。仿真和测试结果表明提出的故障特征提取方法有效的消除了环境噪声的干扰,提高了故障特征提取的准确性和敏感性。
针对动车组制动控制系统的关键部件-传感器在复杂电磁环境下的故障特征提取问题,本文提出了一种基于集成经验模态分解能量熵的故障特征提取新方法。提出的集成经验模态分解方法是自适应的信号分解方法,在时域和频域都具有很高的分辨率,可应用在传感器故障非线性、非稳定性信号的分析处理上,并通过均匀添加高斯白噪声的方法消除了传统的经验模态分解中存在的模态混叠的影响,提高了故障特征提取的准确性和可靠性。
针对动车组制动控制系统故障小样本和噪声干扰性强的特点,本文提出了一种基于改进粒子群优化算法优化的最小二乘支持向量机的故障诊断方法。提出的多群体协同混沌粒子群优化算法克服了标准粒子群优化算法易早熟、后期收敛速度慢和局部搜索精度低的缺陷,充分利用多群体协同进化粒子群优化算法较好的全局搜索能力和混沌粒子群优化算法局部搜索能力强的优势,提出的优化算法具有良好的局部搜索能力和全局寻优能力。函数优化测试和分类性能测试证明了提出的优化算法的优越性。在制动控制单元模拟电路容差软故障和传感器故障的诊断中,利用多群体协同混沌粒子群优化算法对最小二乘支持向量机的结构参数进行优化,提高了故障的诊断精度和速度。试验表明,该故障诊断方法具有良好的泛化能力和鲁棒性,故障分类辨识率更高,速度更快,在小样本条件下性能较其他分类方法更优。
在动车组制动控制系统故障诊断中,最小二乘支持向量机的分类性能受核函数性能影响较大的问题,本文提出了采用混合核函数的方法。对局部性能较好的RBF核函数和全局性能较好的多项式核函数进行有效组合,并利用平衡因子对优化的性能和时间进行调整,提高了故障诊断的速度和精度。
针对动车组制动控制系统中存在多故障模式识别的问题,本文提出基于改进最优二叉树结构的最小二乘支持向量机多分类方法,以类间分离性测度替代最优二叉树结构中的权值。与一对一、一对多、有向无环图等多分类方法相比,提出的改进最优二叉树结构的最小二乘支持向量机不仅分类器数目较少,分类速度较快,而且不存在分类盲区,分类精度更高。数据样本测试和制动控制单元模拟电路容差软故障诊断试验证明了该方法的有效性和优越性。
最后设计开发了基于虚拟仪器和LabVIEW编程语言的动车组制动控制单元自动化测试与诊断系统。试验结果表明该系统可对动车组制动控制单元进行自动化测试和故障诊断,可用于制动控制单元的出厂检测和维护维修,对提高制动控制系统的可靠性和稳定性起到重要作用。
With the popularity of the high-speed railway in China, its operation security has attracted more and more attention. The issue of how to protect the high-speed train security and reliability has become a hot and difficult research subject recently.
Brake control system, as the core subsystem of brake system in electric multiple units (EMUs), plays an important role in the security and reliability of train operation. Thus the research of status monitoring and fault diagnosis for brake control system is particularly important and critical. However, the brake control system failures occurs frequently of EMUs, due to the complexity of the brake control system and the shortage of technical transfer time for digestion and absorption in China, which affect the normal running of the EMUs seriously. With a depth study on potential failures of h EMUs brake control system, the improved fault feature extraction methods and intelligent fault diagnosis methods were put forward in this dissertation to improve the reliability and stability of brake control system. In addition, fault monitoring and diagnosis system was designed and applied to the brake control system of CRH2EMUs. The main researchs and innovations were summarized as follows:
A fault feature extraction method for brake control unit (BCU) analog circuit soft fault of EMUs was proposed based on combined morphological filtering and wavelet packet energy entropy, solving the problem of complex electromagnetic environment of train. Firstly, the fault signals were pretreated to eliminate the Gaussian white noise, the peak pulse and unknown noise by combined mathematical morphological filter. Then the pretreated signals were multiple-level decomposed by wavelet packet method, and then wavelet packet energy entropy was extracted as fault feature vectors. The proposed fault feature extraction method could increase the correctly and sensitivity of BCU fault feature information.
A fault feature extraction method for sensor failure of EMUs brake control system was proposed based on ensemble empirical mode decomposition (EEMD), in order to solve the problem of complex electromagnetic environment of EMUs. The traditional fault feature extraction method for sensor based on empirical mode decomposition (EMD) performed undesirable, because sensor failure output was always non-linear and non-stability and EMD decomposition had shortcoming of mode mixing, which decreased the accuracy of fault feature extraction. By adding uniform white Gaussian noise to EEMD, mode mixing was greatly reduced, and the accuracy and reliability of sensor fault feature extraction was improved.
A least squares support vector machine (LSSVM) fault diagnosis method based on improved particle swarm optimization (PSO) algorithm for EMUs brake control system equipment failure with feature is a small sample size and the noise was proposed. Standard PSO has disadvantage of premature, slow convergence at the later stage and low accuracy of local search. Then,the multi-swarm cooperative chaos particle swarm optimization (MCCPSO) algorithm was proposed through absorbed the advantage of multi-swam cooperative particle swarm optimization(MCPSO) algorithm with good global search performance and chaos particle swarm optimization(CPSO) algorithm with good local search performance. Standard function optimization test and classification performance test had proved the superiority of proposed method. In order to improve the diagnosis accuracy and speed, MCCPSO algorithm was used for optimizing the structure parameters of LSSVM in BCU analog circuit tolerance soft fault diagnosis. The results showed that the fault diagnosis method had better performance, higher fault classification correct rate and faster training and test speed, as well as better classification performance than other classification methods in the small sample.
As the multi-classification performance of LSSVM was influenced enormously by kernel function performance in the fault diagnosis of EMUs brake control system, a mixture kernel function was put forward. The mixture kernel function was combined by RBF kernel function with better local search performance and Polynomial kernel function with better global search performance, and an impact factor was introduced to balance the optimizing performance and optimizing time, which improved the fault diagnosis speed and accuracy effectively.
The improved optimal binary tree (IOBT) structure LSSVM multi-classification method was proposed for multi-fault pattern recognition problems in the EMUs brake control system, and the weight was replaced by class separatory measure. Compared with one-versus-one (O-V-O), one-versus-rest (O-V-R) and decision directed acyclic graph (DDAG) multi-classification methods, the proposed multi-classification method not only needed few classifiers but also classification faster, as well as without classification dead zone and higher classification accuracy. Both the data sample test and BCU analog circuit soft fault diagnosis test had proved the effectiveness and superiority.
The intelligent automatic detection and diagnosis system for EMUs brake control unit was designed and developed based on virtual instrument technology and Lab VIEW programming language. The test results showed that the system could automate detection and diagnosis the EMUs brake control system failures. It could be used for factory testing and maintenance of brake control system, which could improve the reliability and stability of brake control system.
制动控制系统作为动车组制动系统的关键组成部分,能否正常稳定工作,直接影响动车组的安全可靠运行,因此对制动控制系统的状态监测和故障诊断显得尤为重要和关键。由于动车组制动控制系统的复杂性及引进消化吸收的时间不长,制动控制系统故障仍较为多发,严重影响着动车组的正常稳定可靠运行。因此本课题对动车组制动控制系统中关键设备和部件的故障及潜在故障隐患开展深入研究,提出和改进了已有的故障特征提取技术和故障诊断方法,用于动车组制动控制系统关键设备和部件的故障诊断,以提高制动控制系统的可靠性、稳定性和主动安全防护能力。设计开发了制动控制单元自动化测试与故障诊断系统,并运用在CRH2型动车组制动控制系统的监测与故障诊断中,取得了很好的效果。主要研究内容和取得的创新性研究成果主要有以下几个方面：
针对动车组制动控制系统的关键设备-制动控制单元(制动控制器)模拟电路软故障在复杂电磁环境下的故障特征提取问题,本文提出了一种基于形态学滤波-小波包能量熵的故障特征提取新方法。首先采用组合式形态学滤波器对故障信号进行预处理,消除白噪声、尖峰脉冲及未知噪声的干扰；然后采用小波包分析方法对预处理后的故障信号进行多层小波包分解,提取小波包能量熵组成故障特征向量输入到最小二乘支持向量机中进行分类识别。仿真和测试结果表明提出的故障特征提取方法有效的消除了环境噪声的干扰,提高了故障特征提取的准确性和敏感性。
针对动车组制动控制系统的关键部件-传感器在复杂电磁环境下的故障特征提取问题,本文提出了一种基于集成经验模态分解能量熵的故障特征提取新方法。提出的集成经验模态分解方法是自适应的信号分解方法,在时域和频域都具有很高的分辨率,可应用在传感器故障非线性、非稳定性信号的分析处理上,并通过均匀添加高斯白噪声的方法消除了传统的经验模态分解中存在的模态混叠的影响,提高了故障特征提取的准确性和可靠性。
针对动车组制动控制系统故障小样本和噪声干扰性强的特点,本文提出了一种基于改进粒子群优化算法优化的最小二乘支持向量机的故障诊断方法。提出的多群体协同混沌粒子群优化算法克服了标准粒子群优化算法易早熟、后期收敛速度慢和局部搜索精度低的缺陷,充分利用多群体协同进化粒子群优化算法较好的全局搜索能力和混沌粒子群优化算法局部搜索能力强的优势,提出的优化算法具有良好的局部搜索能力和全局寻优能力。函数优化测试和分类性能测试证明了提出的优化算法的优越性。在制动控制单元模拟电路容差软故障和传感器故障的诊断中,利用多群体协同混沌粒子群优化算法对最小二乘支持向量机的结构参数进行优化,提高了故障的诊断精度和速度。试验表明,该故障诊断方法具有良好的泛化能力和鲁棒性,故障分类辨识率更高,速度更快,在小样本条件下性能较其他分类方法更优。
在动车组制动控制系统故障诊断中,最小二乘支持向量机的分类性能受核函数性能影响较大的问题,本文提出了采用混合核函数的方法。对局部性能较好的RBF核函数和全局性能较好的多项式核函数进行有效组合,并利用平衡因子对优化的性能和时间进行调整,提高了故障诊断的速度和精度。
针对动车组制动控制系统中存在多故障模式识别的问题,本文提出基于改进最优二叉树结构的最小二乘支持向量机多分类方法,以类间分离性测度替代最优二叉树结构中的权值。与一对一、一对多、有向无环图等多分类方法相比,提出的改进最优二叉树结构的最小二乘支持向量机不仅分类器数目较少,分类速度较快,而且不存在分类盲区,分类精度更高。数据样本测试和制动控制单元模拟电路容差软故障诊断试验证明了该方法的有效性和优越性。
最后设计开发了基于虚拟仪器和LabVIEW编程语言的动车组制动控制单元自动化测试与诊断系统。试验结果表明该系统可对动车组制动控制单元进行自动化测试和故障诊断,可用于制动控制单元的出厂检测和维护维修,对提高制动控制系统的可靠性和稳定性起到重要作用。
With the popularity of the high-speed railway in China, its operation security has attracted more and more attention. The issue of how to protect the high-speed train security and reliability has become a hot and difficult research subject recently.
Brake control system, as the core subsystem of brake system in electric multiple units (EMUs), plays an important role in the security and reliability of train operation. Thus the research of status monitoring and fault diagnosis for brake control system is particularly important and critical. However, the brake control system failures occurs frequently of EMUs, due to the complexity of the brake control system and the shortage of technical transfer time for digestion and absorption in China, which affect the normal running of the EMUs seriously. With a depth study on potential failures of h EMUs brake control system, the improved fault feature extraction methods and intelligent fault diagnosis methods were put forward in this dissertation to improve the reliability and stability of brake control system. In addition, fault monitoring and diagnosis system was designed and applied to the brake control system of CRH2EMUs. The main researchs and innovations were summarized as follows:
A fault feature extraction method for brake control unit (BCU) analog circuit soft fault of EMUs was proposed based on combined morphological filtering and wavelet packet energy entropy, solving the problem of complex electromagnetic environment of train. Firstly, the fault signals were pretreated to eliminate the Gaussian white noise, the peak pulse and unknown noise by combined mathematical morphological filter. Then the pretreated signals were multiple-level decomposed by wavelet packet method, and then wavelet packet energy entropy was extracted as fault feature vectors. The proposed fault feature extraction method could increase the correctly and sensitivity of BCU fault feature information.
A fault feature extraction method for sensor failure of EMUs brake control system was proposed based on ensemble empirical mode decomposition (EEMD), in order to solve the problem of complex electromagnetic environment of EMUs. The traditional fault feature extraction method for sensor based on empirical mode decomposition (EMD) performed undesirable, because sensor failure output was always non-linear and non-stability and EMD decomposition had shortcoming of mode mixing, which decreased the accuracy of fault feature extraction. By adding uniform white Gaussian noise to EEMD, mode mixing was greatly reduced, and the accuracy and reliability of sensor fault feature extraction was improved.
A least squares support vector machine (LSSVM) fault diagnosis method based on improved particle swarm optimization (PSO) algorithm for EMUs brake control system equipment failure with feature is a small sample size and the noise was proposed. Standard PSO has disadvantage of premature, slow convergence at the later stage and low accuracy of local search. Then,the multi-swarm cooperative chaos particle swarm optimization (MCCPSO) algorithm was proposed through absorbed the advantage of multi-swam cooperative particle swarm optimization(MCPSO) algorithm with good global search performance and chaos particle swarm optimization(CPSO) algorithm with good local search performance. Standard function optimization test and classification performance test had proved the superiority of proposed method. In order to improve the diagnosis accuracy and speed, MCCPSO algorithm was used for optimizing the structure parameters of LSSVM in BCU analog circuit tolerance soft fault diagnosis. The results showed that the fault diagnosis method had better performance, higher fault classification correct rate and faster training and test speed, as well as better classification performance than other classification methods in the small sample.
As the multi-classification performance of LSSVM was influenced enormously by kernel function performance in the fault diagnosis of EMUs brake control system, a mixture kernel function was put forward. The mixture kernel function was combined by RBF kernel function with better local search performance and Polynomial kernel function with better global search performance, and an impact factor was introduced to balance the optimizing performance and optimizing time, which improved the fault diagnosis speed and accuracy effectively.
The improved optimal binary tree (IOBT) structure LSSVM multi-classification method was proposed for multi-fault pattern recognition problems in the EMUs brake control system, and the weight was replaced by class separatory measure. Compared with one-versus-one (O-V-O), one-versus-rest (O-V-R) and decision directed acyclic graph (DDAG) multi-classification methods, the proposed multi-classification method not only needed few classifiers but also classification faster, as well as without classification dead zone and higher classification accuracy. Both the data sample test and BCU analog circuit soft fault diagnosis test had proved the effectiveness and superiority.
The intelligent automatic detection and diagnosis system for EMUs brake control unit was designed and developed based on virtual instrument technology and Lab VIEW programming language. The test results showed that the system could automate detection and diagnosis the EMUs brake control system failures. It could be used for factory testing and maintenance of brake control system, which could improve the reliability and stability of brake control system.
引文
[1]张曙光.CRH2型动车组[M].北京：中国铁道出版社,2008.
[2]黄采伦,樊晓平,陈特放.列车故障在线诊断技术及应用[M].北京：国防工业出版社,2006.
[3]黄栋杰.200公里级CRH2型动车组制动控制系统的研究[D].成都：西南交通大学,2010.
[4]李益民.动车组制动系统[M].成都：西南交通大学出版社,2008.
[5]赵进.动车组制动实验系统研究[D].北京：北京交通大学,2008.
[6]铁道部运输局编.CRH系列动车组故障处理汇编[M].北京：中国铁道出版社,2008.
[7]铁道部运输局编.CRH2型动车组途中应急故障处理手册[M].北京：中国铁道出版社,2009.
[8]何学文.基于支持向量机的故障智能诊断理论与方法研究[D].长沙：中南大学,2004.
[9]Lin Lixin,Jiang Xinhua,Huang Zhiwu.The neural network technology applied on fault detection of locomotive converter[C].2010 Chinese Control and Decision Conference,Xuzhou,2010: 2668-2672.
[10]Lei Yaguo, He Zhengjia, Zi Yanyagn.EEMD method and WNN for fault diagnosis of locomotive roller bearing [J].Expert Systems with Applications,211,38(6):7334-7341.
[11]T.L.M.Morgado, C.M.Branco, V.Infante.A failure study of housing of the gearboxes of series 2600 locomotives of the Portuguese railway company [J]. Engineering Failure Analysis,2008, 15(1-2):154-164.
[12]张永春.电力机车制动系统实时监测与故障诊断研究[D].成都：西南交通大学,2005.
[13]刘晓卉.电力机车制动系统实时监测与故障诊断[D].成都：西南交通大学,2007.
[14]林立新,蒋新华,黄志武.SS7E电力机车开关量智能故障诊断[J].铁道学报,2008,30(2)：88-93.
[15]汪子皓,莫易敏.基于故障诊断二叉树的内燃机车故障诊断专家系统[J].中国铁道科学,2008,29(5)：98-101.
[16]聂晔.动车组转向架轴承可靠性分析与故障诊断的技术研究[D].长沙：中南大学,2011.
[17]杨建伟,蔡国强,姚德臣,等.基于小波包变换和BP神经网络的铁道车辆滚动轴承故障诊断方法[J].中国铁道科学,2010,31(6)：68-73.
[18]严书荣.列车制动故障诊断专家系统关键技术研究及应用[D].大连：大连交通大学,2011.
[19]Anil Varma, Nicholas Roddy.ICARUS:Design and Deployment of a Case-based Reasoning System for Locomotive Diagnostics [J].Engineering Applications of Artificial Intelligence, 1999,12(6):681-690.
[20]赵明.基于案例推理的机车故障诊断专家系统研究[D].长沙：中南大学,2004.
[21]赵治平.基于多智能体的8G型电力机车故障诊断系统的研制[D].长沙：中南大学,2009.
[22]Yang Yingze, Gui Weihua, Huang Zhiwu.On-line diagnosis for sync brake systems of combined train based on multi-agent technique[C].The 2nd IEEE International Conference on Computer Science and Information Technology, Beijing,2009:129-133.
[23]Hu Lingling, Zhang Santong.Fault diagnosis model of the diesel locomotive air brake system based on Bayesian network[C].2010 International Conference on Logistics Engineering and Intelligent Transportation Systems, Wuhan,2010:312-314.
[24]侯文明.HXD1型机车制动系统故障在线诊断技术的研究与应用[D].长沙：中南大学,2010.
[25]耿永强.基于支持向量机的机车轴承故障诊断系统的研究[D].长沙：中南大学,2008.
[26]Luo Jianhui,Madhavi Namburu,Krishna R.Pattipati,et al.Integrated model-based and data-driven diagnosis of automotive antilock braking systems[J].IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans.2010,40(2):321-336.
[27]董锡明.现代高速列车技术[M].北京：中国铁道出版社,2006.
[28]刘劲松,张双林.KAX1客车行车安全监测诊断系统的安全保障作用[J].铁道机车车辆,2005,25(4)：60-61.
[29]黄志武,刘建刚,刘伟荣,等.基于解析冗余关系的法维莱制动机系统故障诊断技术[J].铁道学报,2013,35(3)：29-35.
[30]刘作琪,刘英臣,臧朋波,等.高速动车组制动测试系统[P].中国：CN201120073686.9,2011.
[31]方科挺.高速列车应急故障模拟与维修培训系统[D].北京：北京交通大学,2008.
[32]黄学文,刘春明,冯璨,等.CRH3高速动车组故障诊断系统[J].计算机集成制造系统,2010,16(10)：2311-2318.
[33]张泰基.CRH1型动车组系统监控与故障检测的应用研究[D].青岛：青岛大学,2009.
[34]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[J].电工技术学报,2007,22(6)：12-16.
[35]赵林海,蔡伯根,邱宽民,等.基于HHT、DBWT的无绝缘轨道电路补偿电容故障诊断[J].铁道学报,2011,33(3)：49-54.
[36]赵林海,冉义奎,穆建成.基于遗传算法的无绝缘轨道电路故障综合诊断方法[J].中国铁道科学,2010,31(3)：107-114.
[37]宋国明.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[D].成都：电子科技大学,2010.
[38]唐人亨.模拟系统的自动故障诊断[M].北京：高等教育出版社,1991.
[39]钟建林,何友,任献彬.基于波形相似度的容差模拟电路软故障诊断[J].电工技术学报,2012,27(8)：222-229.
[40]邓勇,师奕兵,张伟.基于FRWT的模拟电路早期故障诊断[J].仪器仪表学报,2012,33(3)：555-560.
[41]徐宇亮,孙际哲,陈西宏,等.采用多目标粒子群算法的模拟电路故障诊断研究[J].西安交通大学学报,2012,46(6)：1-6.
[42]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[D].长沙：湖南大学,2006.
[43]Mao Xianbai, Wang Liheng, Li Changxi.SVM classifier for analog fault diagnosis using fractal features[C].2th International Symposium on Intelligent Information Technology Application, Wuhan,2008:553-557.
[44]付克昌.基于结构优化PCA的传感器故障诊断方法及其应用研究[D].杭州：浙江大学,2007.
[45]李蔚,陈特放,陈春阳,等.列车电气系统分布式多传感器异常检测理论研究[J].铁道学报,2010,32(5)：70-76.
[46]Richard Dorr, Frederic Kratz, Jose Ragot,et al. Detection,Isolation,and Identification of Sensor Faults in Nuclear Power Plants[J].IEEE Transactions on Control Systems Technology,1997, 5(1):42-60.
[47]Zhu Kunpeng,Wong Yokesan,Hong Geoksoon.Wavelet analysis of sensor signals for tool condition monitoring:a review and some new results[J].International Journal of Machine Tools and Manufacture,2009,49(7-8):537-553.
[48]李欢欢,司风琪,徐治皋.一种基于鲁棒自联想神经网络的传感器故障诊断方法[J].中国电机 工程学报,2012,32(14)：116-121.
[49]胡明江,王忠,祁利巧,等.柴油机低温启动工况的传感器在线诊断[J].振动、测试与诊断,2010,30(3)：286-290.
[50]陈楚瑶,朱大奇.神经网络主元分析的传感器故障诊断方法[J].系统工程与电子技术,2010,32(7)：1549-1553.
[51]司风琪,李欢欢,徐治皋.基于鲁棒输入训练神经网络的非线性多传感器故障诊断方法及其应用[J].东南大学学报(自然科学版),2011,41(3)：574-578.
[52]吴锋,潘书澜,邹博文.基于BP神经网络的电控汽油机传感器故障在线诊断技术研究[J].内燃机学报,2006,24(4)：369-373.
[53]冯志刚,王祁,徐涛,等.基于小波包和支持向量机的传感器故障诊断方法[J].南京理工大学学报(自然科学版),2008,32(5)：609-615.
[54]冯志刚,王祁,信太克规.基于EMD和SVM的传感器故障诊断方法[J].哈尔滨工业大学学报,2009,41(5)：59-63.
[55]Cheng Junsheng, Yu Dejie, Yang Yu.Application of support vector regression machines to the processing of end effects of Hilbet-Huang transform [J]. Mechanical Systems and Signal Processing,2012,21(3):1197-1211.
[56]Lin Dachao, Guo Zhanglin, An Fengping, et al. Elimination of end effects in empirical mode decomposition by mirror image coupled with support vector regression [J].Mechanical Systems and Signal Processing,2012,31(1):13-18.
[57]Lei Yaguo,Lin Jing,He Zhengjia,et al.A review on ensemble empirical mode decomposition in fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2013,35 (1-2):108-126.
[58]Hsu Chunchin,Chen Muchen,Chen Longsheng.Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring[J].Expert Systems with Applications,2010,37(4): 3264-3273.
[59]Xiao Yingqun, Feng Lianggui.A novel linear ridgelet network approach for analog fault diagnosis using wavelet-based fractal analysis and kernel PCA as preprocessors [J]. Measurement,2012,45(3):297-310.
[60]Xiao Yingqun, He Yigang.A novel approach for analog fault diagnosis based on neural network and improved kernel PCA [J].Neurocomputing,2011,74(7):1102-1115.
[61]Zhu Daqi, Bai Jie, Yang Simon X.A multi-fault diagnosis method for sensor systems based on principle component analysis [J].Sensors,2010,10:241-253.
[62]Sun Weixiang, Chen Jin, Li Jiangqing.Decision tree and PCA-based fault diagnosis of rotating machinery [J].Mechanical Systems and Signal Processing,2007,21(3):1300-1317.
[63]Yu Jianbo.Local and global principal component analysis for process monitoring [J].Journal of Process Control,2012,22(7):1358-1373.
[64]Issam Ben Khediri,Mohamed Limam,Claus Weihs.Wariable window adaptive kernel principal component analysis for nonlinear nonstationary process monitoring[J].Computers&Industrial Engineering,2011,61 (3):437-446.
[65]M.A.Bin Shams, H.M. Budman, T.A.Duever.Fault detection, identification and diagnosis using CUSUM based PCA [J].Chemical Engineering Science,2011,66(20):4488-4498.
[66]沈路.数学形态学在机械故障诊断中的应用研究[D].杭州：浙江大学,2010.
[67]林勇,周晓军,张文斌,等.基于形态小波理论和双谱分析的滚动轴承故障诊断[J].浙江大学学报(工学版),2010,44(3)：432-439.
[68]赵静,何正友,钱清泉.利用广义形态滤波与差分熵的电能质量扰动检测[J].中国电机工程学报,2009,29(7)：121-127.
[69]岳蔚,刘沛.基于数学形态学消噪的电能质量扰动检测方法[J].电力系统自动化,2002,(26)7：13-17.
[70]张立国,杨瑾,李晶,等.基于小波包和数学形态学结合的图像特征提取方法[J].仪器仪表学报,2010,31(10)：2285-2290.
[71]陈维荣,冯倩,张健,等.受电弓滑板状态监测的图像目标提取[J].西南交通大学学报,2010,45(1)：59-64.
[72]申俊琦,胡绳荪,冯胜强,等.基于数学形态学的寒风图像边缘提取[J].天津大学学报,2010,43(4)：373-377.
[73]王耀琦,王晓鹏,张忠林.一种低信噪比环境下的基音检测方法[J].铁道学报,2012,34(2)：58-62.
[74]Zhang Jun,Wang Heping.A new approach of pitch detection based on morphology filter and wavelet transform[C].2010 2th International Workshop on Education Technology and Computer Science,Wuhan,2010:751-753.
[75]李天云,杨梅,周喜超,等.基于小波变换和数学形态学的局部放电信号分析方法[J].电网技术,2007,31(6)：56-60.
[76]龚炜,石青云,程民德.数字空间的数学形态学-理论及应用[M].北京：科学出版社,1997.
[77]张文斌,杨辰龙,周晓军.形态滤波方法在振动信号降噪中的应用[J].浙江大学学报(工学版),2009,43(11)：2096-2099.
[78]李天云,郭跃霞,王静,等.基于数学形态学和短窗功率算法的电能质量扰动检测方法[J].电力自动化设备,2008,28(7)：37-40.
[79]胡爱军,唐贵基,安连锁.基于数学形态学的旋转机械振动信号降噪方法[J].机械工程学报,2006,42(4)：127-130.
[80]安源,田录林,刘家军.基于形态学-小波识别变压器励磁涌流[J].电力自动化设备,2008,28(7)：41-45.
[81]Maragos P, Schafer R. Morphological Filters. Part Ⅰ:Their Set-theoretic Analysis and Relation to Linear Shift-invariant Filters [J]. IEEE Transactions on ASSP,1987,35 (8):1153-1169.
[82]Maragos P,Schafer R.Morphological Filters. Part Ⅱ:Their Relation to Median, Order-statistic, and Stack Filters [J].IEEE Transactions on ASSP,1987,35(8):1170-1184.
[83]Stevenson R.L, Arce G. Morphological filters:statistical and further syntactic properties [J]. IEEE Transactions on Circuits and Systems,1987,34(11):1292-1305.
[84]司大军,束洪春,陈学允,等.输电线路雷击的电磁暂态特征分析及其识别方法研究[J].中国电机工程学报,2005,25(7)：64-69.
[85]张全明,刘会金,周新启,等.电能质量信号的小波软阈值去噪方法[J].高电压技术,2006,32(1)：99-101.
[86]程正兴,杨守志,冯晓霞.小波分析的理论算法进展和应用[M].北京：国防工业出版社,2007.
[87]王慧琴.小波分析与应用[M].北京：北京邮电大学出版社,2011.
[88]张彬,杨风暴.小波分析方法与其应用[M].北京：国防工业出版社,2011.
[89]Biswaranjan Behera.Wavelet packets associated with nonuniform multiresolution analyses [J].Journal of Mathematical Analysis and Applications,2007,328(2):1237-1246.
[90]王旭红,何怡刚.基于小波包和Elman神经网络的异步电机转子断条故障诊断方法[J].湖南大学学报(自然科学版),2010,37(5)：45-48.
[91]Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for non linear non-stationary time series analysis[J]. Proceedings of the Royal Society,1998, 454(1971):903-995.
[92]Wu Zhaohua, Huang Norden.A study of the characteristics of white noise using the empirical mode decomposition method [J].Proceddings:Mathematical Physical and Engineering Science, 2004,460(2046):1597-1611.
[93]Wu Zhaohua, Huang Norden E.Ensemble empirical mode decomposition:a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis,2009,1(1):1-41.
[94]陈略,訾艳阳,何正嘉,等.总体平均经验模式分解与1.5维谱方法的研究[J].西安交通大学学报,2009,43(5)：94-98.
[95]蒋少华,桂卫华,杨春华,等.基于RS与LSSVM多分类法的故障诊断方法及其应用[J].中南大学学报(自然科学版),2009,40(2)：447-451.
[96]Suykens J A K, Vandewalle J.Least squares support vector machine classifiers [J]. Neural Processing Letters,1999,9(3):293-300.
[97]Suykens J A K, Lukas L, Vandewalle J.Sparse least squares vector machine classifiers[C]. Proceedings of the European Symposium on Artificial Neural Networks, Bruges Belgium,2000: 37-42.
[98]Lukas L, Suykens J A K, Vandewalle J.Least squares support vector machines classifiers:A multi two-spiral benchmark problem[C].Proceedings of the Indonesian Student Scientific Meeting,Manchester,UK,2001:289-292.
[99]彭文季,罗兴锜,郭鹏程,等.基于最小二乘支持向量机和信息融合技术的水电机组振动故障诊断[J].中国电机工程学报,2007,27(23)：86-92.
[100]Mohammadreza Afshin. Application of least squares support vector machines in medium-term load forecasting [D].Canada:Ryerson University,2007.
[101]王平,刘玉涛.基于GA和LSSVM的时间序列预测[J].华北电力大学学报(自然科学版),2009,36(4)：100-103.
[102]李大川,忻展红.大型呼叫中心人工呼入量的最小二乘支持向量机模型[J].控制理论与应用,2009,26(7)：815-818.
[103]夏克文,董瑶,杜红斌.基于改进PSO算法的LSSVM油层识别模型[J].控制与决策,2007,22(12)：1385-1389.
[104]Liu Qibin,Yang Minlin,Lei Jing,et al.Modeling and optimizing parabolic trough solar collector systems using the least squares support vector machine method[J].Solar Energy,2012, 86(7):1973-1980.
[105]C.M.Vong, P.K.Wong.Engine ignition signal diagnosis with wavelet packet transforms and multi-class least squares support vector machines [J].Expert Systems with Applications,2011, 38(7):8563-8570.
[106]X.C.Guo, J.H.Yang, C.GWu, et al.A novel LSSVM hyper-parameter selection based on particle swarm optimization [J].Neurocomputing,2008,71(16-18):3211-3215.
[107]Suyens.J.A.K, Van Gestel.T, De Brabanter.J, et al.Least Squares Support Vector Manchine [M]. Singapore:World Scientific Publishing,2002.
[108]Reich Y, Barai S.V.Evaluating machine learning models for engineering problems [J].Artifitial Intelligence in Engineering,1999,13(3):257-272.
[109]S. Sathiya Keerthi.Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms[J].IEEE Transactions on Neural Networks,2002,13(5):1225-1229.
[110]Olivier Chapelle.Alexander Zien. Semi-supervised classification by low density separation[C]. Proceedings of the 10th International Workshop on Artificial Telligence and Statistics, Hastings, Christ Church, Barbados,2005:57-64.
[111]李娜,杨恒,屈志坚,等.基于改进最小二乘支持向量机的电力机车牵引电机建模[J].铁道学报,2011,33(3)：23-27.
[112]李锋,汤宝平,刘文艺.遗传算法优化最小二乘支持向量机的故障诊断[J].重庆大学学报,2010,33(12)：14-20.
[113]隋文涛,路长厚,’Wilson Wang,等.基于模拟退火与LSSVM的轴承故障诊断[J].振动、测试与诊断,2010,30(2)：119-122.
[114]耿立艳,张天伟,赵鹏.基于灰色关联分析的LSSVM铁路货运量预测[J].铁道学报,2012,34(3)：1-6.
[115]龙文,梁昔明,龙祖强.基于混合PSO优化的LSSVM锅炉烟气含氧量预测控制[J].中南大学学报(自然科学版),2012,43(3)：980-985.
[116]黄英杰,姚锡凡,古耀达.基于熵的混合粒子群算法在柔性调度中的应用[J].湖南大学学报(自然科学版),2012,39(3)：48-52.
[117]HSU.C, Lin C.A comparison of methods for multiclass support vector machines [J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[118]T. G. Dietterich, GBakiri. Solving multiclass learning problems via error-correcting output codes [J].Journal of Artificial Intelligence Research,1995,2(1):263-286.
[119]Erin L. Allwein, Robert E. Schapire, Yoram Singer. Reducing multiclass to binary:a unifying approach for margin classifiers [J]. Journal of Machine Learning Research,2000,1:113-141.
[120]邱绵浩,王自营,安钢,等.基于核主元分析与纠错输出编码SVM的齿轮故障诊断[J].振动与冲击,2009,28(5)：1-5.
[121]Platt John C.,Cristianini Nello,Shawe-Taylor John.Large margin DAGs for multiclass classification[C].Proceddings of Neural Information Processing System, Massachusetts:MIT Press,2000:547-553.
[122]易辉,宋晓峰,姜斌,等.基于结点优化的决策导向无环图支持向量机及其在故障诊断中的应用[J].自动化学报,2010,36(3)：427-432.
[123]王艳,陈欢欢,沈毅.有向无环图的多类支持向量机分类算法[J].电机与控制学报,2011,15(4)：85-89.
[124]Hsu Chinwei, Lin Chinjen. A comparison of methods for multi-class support vector machines [J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[125]Yang Chihcheng, Lee Wanjui, Lee Shiejue. Learning of kernel functions in support vector machines[C].2006 International Joint Conference on Neural Networks, Vancouver, Canada, 2006:1150-1155.
[126]杨琳,王从庆,缪鹏,等.基于Huffman最优二叉树支持向量机的舱音记录器背景信号识别[J].宇航学报,2011,32(6)：1428-1434.
[127]崔江,王友仁.模拟电路故障的一种聚类二叉树支持向量机诊断新方法[J].仪器仪表学报,2008,29(10)：2047-2051.
[128]薛宁静.多类支持向量机分类器对比研究[J].计算机工程与设计,2011,32(5)：1792-1795.
[129]Yuan Haiying, Chen Guangju, Xie Yongle.Feature evaluation and extraction based on neural network in analog circuit fault diagnosis [J].Journal of Systems Engineering and Electronics. 2007,18(2):434-436.
[130]Long Bing,Huang Jianguo,Tian Shulin.Least squares support vector machine based on analog-circuit fault diagnosis using wavelet transform preprocessor[C]. International Conference on Communications,Circuits and Systems,Fujian,2008:1026-1029.
[131]Mehran Aminian, Farzan Aminian. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor [J]. IEEE Transactions on Circuits and Systems Ⅱ,2000, 47(2):151-156.
[132]Robert Spina, Shambhu Upadhyaya. Linear circuit fault diagnosis using neuromorphic analyzers [J]. IEEE Transactions on Circuits and Systems Ⅱ,1997,44(3):188-196.
[133]Farzan Aminian,Mehran Aminian,H. W. Collins. Analog fault diagnosis of actual circuits using neural networks[J]. IEEE Transactions on Instrumentation and Measurement,2002,51(3): 544-550.
[134]王佩丽,彭敏放,杨易旻,等.应用模糊最优小波包和LSSVM的模拟电路诊断[J].仪器仪表学报,2010,31(6)：1282-1288.
[135]孙永奎,陈光聥,李辉.基于自适应小波分解和SVM的模拟电路故障诊断[J].仪器仪表学报,2008,29(10)：2105-2109.
[136]马超,陈西宏,徐宇亮,等.广义邻域粗集下的集成特征选择及其选择性集成算法[J].西安交通大学学报,2011,45(6)：34-39.
[137]鲁昌华,汪涌,王玲飞.基于PCA和SVM的模拟电路故障诊断[J].电子测量与仪器学报,2006,22(3)：64-68.
[138]内田清五,陈贺,李毅,等.日本新干线列车制动系统[M].北京：中国铁道出版社,2004.
[139]饶忠,彭俊彬.列车制动[M](第二版).北京：中国铁道出版社,2010.
[140]Zhang Jianqiu, Yan Yong. A wavelet-based approach to abrupt fault detection and diagnosis of sensor [J]. IEEE Transactions on Instrumentation and Measurement,2001,50(5):1389-1396.
[141]Zhu Yonghua,Jin Xinqiao,Du Zhimin.Fault diagnosis for sensors in air handling based on neural network pre-processed by wavelet and fractal [J].Energy and Buildings,2012,44:7-16.
[142]徐涛,王祁.基于小波包神经网络的传感器故障诊断方法[J].传感技术学报,2006,19(4)：1060-1064.
[143]张超,陈建军,郭迅.基于EEMD能量熵和支持向量机的齿轮故障诊断方法[J].中南大学学报(自然科学版),2012,43(3)：932-939.
[144]Vapnik V.An overview of statistical learning theory [J].IEEE Transaction on Neural Networks, 1999,10(5):988-999.
[145]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1)：32-42.
[146]Zhang Sheng, Liu Jian, Tian Jinwen. An SVM-based small target segmentation and clustering approach[C].Proceedings of the 3rd International Conference on Machine Leaning and Cybernetics, Shanghai,2004:3318-3323.
[147]Smits G. F, Jordan E. M. Improved SVM regeression using mixtures of kernels [C]. IEEE Proceedings of the 2002 International Joint Conference on Neural Networks, Honolulu, 2002:2785-2790.
[148]M.Elangovan, V.Sugumaran, K.I.Ramachandran, S.Ravikumar.Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool [J].Expert Systems with Applications,2011,38(12):15202-15207.
[149]M.Zavar, S.Rahati, M.R.Akbarzadeh, et al.Evolutionary model selection in a waelet based support vector machine for automated seizure detection [J].Expert Systems with Application, 2011,38(9):10751-10758.
[150]郑志成,徐卫亚,徐飞,等.基于混合核函数PSO-LSSVM的边坡变形预测[J].岩土力学,2012,33(5)：1421-1427.
[151]Kennedy J, Eberhart.R.C.Particle swarm optimization[C].Proceedings IEEE International Conference Neural Networks, NJ:IEEE Press,1995:1942-1948.
[152]Kennedy.J, Eberhart.R.C.A discrete binary version of the particle swarm algorithm[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, NJ:IEEE Press,1997:4104-4108.
[153]James Kennedy, Russell C.Eberhart, Shi Yuhui.Swarm Intelligence [M].San Francisco, USA: Morgan Kaufmann,2001.
[154]Shi Yuhui Eberhart R. A modified particle swarm optimizer[C]. Proceedings of IEEE International Conference on Evolutionary Computation, Piscataway, NJ:IEEE Press,1998: 69-73.
[155]Shi Yuhui, Russell C. Eberhart. Empirical study of particle swarm optimization [C]. Proceedings of Congress on Evolutionary Computation, Piscataway, NJ:IEEE Press,1999: 1945-1950.
[156]Russell C. Eberhart, Shi Yuhui.Comparing inertia weights and constriction factors in particle swarm optimization[C].Proceedings of the Congress on Evolutionary Computing, NJ:IEEE Service Center,2000:84-88.
[157]魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究[D].太原：中北大学,2009.
[158]杨维,李岐强.粒子群优化算法综述[J].中国工程科学,2004,(6)：87-94.
[159]G.F.Smits, E.M.Jordaan.Improved SVM regression using mixtures of kernels [C].Proceedings of the 2002 International Jiont Conference on Neural Networks, NJ:IEEE Service Center, 2002:2785-2790.
[160]Gao Ying, Xie Shengli. Chaos particle swarm optimization algorithm [J]. Computer Science, 2004,31(8):13-15.
[161]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002.
[162]Tang Xianlun, Zhuang Ling, Cai Jun, et al. Multi-fault classification based on support vector machine trained by chaos particle swarm optimization [J]. Knowledg -Based Systems,2010, 23(5):486-490.
[163]Liang Wei, Zhang Laibin, Wang Mingda. The chaos differential evolution optimization algorithm and its application to support vector regression machine [J]. Journal of Software, 2011,6(2):1297-1304.
[164]Zhao Chenglin, Sun Xuebin, Sun Songlin, et al. Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine[J], Expert Systems with Applications,2011,38(8):9908-9912.
[165]杜文莉,张海龙,钱锋.融合和声搜索的混沌粒子群优化算法及工业应用[J].清华大学学报(自然科学版),2012,52(3)：325-330.
[166]Jiang Huimin, C.K.Kwong, Chen Zengqiang, et al.Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control [J].Expert Systems with Applications,2012,39(1):194-201.
[167]Niu Ben, Zhu Yunlong, He Xiaoxian, et al. MCPSO:A multi-swarm cooperative particle swarm optimizer [J].Applied Mathematics and Computation,2007,185(2):1050-1062.
[168]Niu Ben, Zhu Yunlong, He Xiaoxian, et al. A multi-swarm optimizer based fuzzy mpdeling approach for dynamic systems processing [J].Neurocomputing,2008,71 (7-9):1436-1448.
[169]牛奔,李丽.基于MCPSO算法的BP神经网络训练[J].深圳大学学报理工版,2009,26(2)：147-150.
[170]李丽,牛奔.粒子群优化算法[M].北京：冶金工业出版社,2009.
[171]赵云涛,王京,宋勇,等.具有转换函数的均匀差分进化算法及其性能分析[J].控制理论与应用,2009,26(9)：1014-1018.
[172]纪震,廖惠连,吴青华.粒子群算法及应用[M].北京：科学出版社,2009.
[173]郭军,刘和平,刘平.基于大电流检测的霍尔传感器应用[J].传感器与微系统,2011,30(3)：142-145.
[2]黄采伦,樊晓平,陈特放.列车故障在线诊断技术及应用[M].北京：国防工业出版社,2006.
[3]黄栋杰.200公里级CRH2型动车组制动控制系统的研究[D].成都：西南交通大学,2010.
[4]李益民.动车组制动系统[M].成都：西南交通大学出版社,2008.
[5]赵进.动车组制动实验系统研究[D].北京：北京交通大学,2008.
[6]铁道部运输局编.CRH系列动车组故障处理汇编[M].北京：中国铁道出版社,2008.
[7]铁道部运输局编.CRH2型动车组途中应急故障处理手册[M].北京：中国铁道出版社,2009.
[8]何学文.基于支持向量机的故障智能诊断理论与方法研究[D].长沙：中南大学,2004.
[9]Lin Lixin,Jiang Xinhua,Huang Zhiwu.The neural network technology applied on fault detection of locomotive converter[C].2010 Chinese Control and Decision Conference,Xuzhou,2010: 2668-2672.
[10]Lei Yaguo, He Zhengjia, Zi Yanyagn.EEMD method and WNN for fault diagnosis of locomotive roller bearing [J].Expert Systems with Applications,211,38(6):7334-7341.
[11]T.L.M.Morgado, C.M.Branco, V.Infante.A failure study of housing of the gearboxes of series 2600 locomotives of the Portuguese railway company [J]. Engineering Failure Analysis,2008, 15(1-2):154-164.
[12]张永春.电力机车制动系统实时监测与故障诊断研究[D].成都：西南交通大学,2005.
[13]刘晓卉.电力机车制动系统实时监测与故障诊断[D].成都：西南交通大学,2007.
[14]林立新,蒋新华,黄志武.SS7E电力机车开关量智能故障诊断[J].铁道学报,2008,30(2)：88-93.
[15]汪子皓,莫易敏.基于故障诊断二叉树的内燃机车故障诊断专家系统[J].中国铁道科学,2008,29(5)：98-101.
[16]聂晔.动车组转向架轴承可靠性分析与故障诊断的技术研究[D].长沙：中南大学,2011.
[17]杨建伟,蔡国强,姚德臣,等.基于小波包变换和BP神经网络的铁道车辆滚动轴承故障诊断方法[J].中国铁道科学,2010,31(6)：68-73.
[18]严书荣.列车制动故障诊断专家系统关键技术研究及应用[D].大连：大连交通大学,2011.
[19]Anil Varma, Nicholas Roddy.ICARUS:Design and Deployment of a Case-based Reasoning System for Locomotive Diagnostics [J].Engineering Applications of Artificial Intelligence, 1999,12(6):681-690.
[20]赵明.基于案例推理的机车故障诊断专家系统研究[D].长沙：中南大学,2004.
[21]赵治平.基于多智能体的8G型电力机车故障诊断系统的研制[D].长沙：中南大学,2009.
[22]Yang Yingze, Gui Weihua, Huang Zhiwu.On-line diagnosis for sync brake systems of combined train based on multi-agent technique[C].The 2nd IEEE International Conference on Computer Science and Information Technology, Beijing,2009:129-133.
[23]Hu Lingling, Zhang Santong.Fault diagnosis model of the diesel locomotive air brake system based on Bayesian network[C].2010 International Conference on Logistics Engineering and Intelligent Transportation Systems, Wuhan,2010:312-314.
[24]侯文明.HXD1型机车制动系统故障在线诊断技术的研究与应用[D].长沙：中南大学,2010.
[25]耿永强.基于支持向量机的机车轴承故障诊断系统的研究[D].长沙：中南大学,2008.
[26]Luo Jianhui,Madhavi Namburu,Krishna R.Pattipati,et al.Integrated model-based and data-driven diagnosis of automotive antilock braking systems[J].IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans.2010,40(2):321-336.
[27]董锡明.现代高速列车技术[M].北京：中国铁道出版社,2006.
[28]刘劲松,张双林.KAX1客车行车安全监测诊断系统的安全保障作用[J].铁道机车车辆,2005,25(4)：60-61.
[29]黄志武,刘建刚,刘伟荣,等.基于解析冗余关系的法维莱制动机系统故障诊断技术[J].铁道学报,2013,35(3)：29-35.
[30]刘作琪,刘英臣,臧朋波,等.高速动车组制动测试系统[P].中国：CN201120073686.9,2011.
[31]方科挺.高速列车应急故障模拟与维修培训系统[D].北京：北京交通大学,2008.
[32]黄学文,刘春明,冯璨,等.CRH3高速动车组故障诊断系统[J].计算机集成制造系统,2010,16(10)：2311-2318.
[33]张泰基.CRH1型动车组系统监控与故障检测的应用研究[D].青岛：青岛大学,2009.
[34]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[J].电工技术学报,2007,22(6)：12-16.
[35]赵林海,蔡伯根,邱宽民,等.基于HHT、DBWT的无绝缘轨道电路补偿电容故障诊断[J].铁道学报,2011,33(3)：49-54.
[36]赵林海,冉义奎,穆建成.基于遗传算法的无绝缘轨道电路故障综合诊断方法[J].中国铁道科学,2010,31(3)：107-114.
[37]宋国明.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[D].成都：电子科技大学,2010.
[38]唐人亨.模拟系统的自动故障诊断[M].北京：高等教育出版社,1991.
[39]钟建林,何友,任献彬.基于波形相似度的容差模拟电路软故障诊断[J].电工技术学报,2012,27(8)：222-229.
[40]邓勇,师奕兵,张伟.基于FRWT的模拟电路早期故障诊断[J].仪器仪表学报,2012,33(3)：555-560.
[41]徐宇亮,孙际哲,陈西宏,等.采用多目标粒子群算法的模拟电路故障诊断研究[J].西安交通大学学报,2012,46(6)：1-6.
[42]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[D].长沙：湖南大学,2006.
[43]Mao Xianbai, Wang Liheng, Li Changxi.SVM classifier for analog fault diagnosis using fractal features[C].2th International Symposium on Intelligent Information Technology Application, Wuhan,2008:553-557.
[44]付克昌.基于结构优化PCA的传感器故障诊断方法及其应用研究[D].杭州：浙江大学,2007.
[45]李蔚,陈特放,陈春阳,等.列车电气系统分布式多传感器异常检测理论研究[J].铁道学报,2010,32(5)：70-76.
[46]Richard Dorr, Frederic Kratz, Jose Ragot,et al. Detection,Isolation,and Identification of Sensor Faults in Nuclear Power Plants[J].IEEE Transactions on Control Systems Technology,1997, 5(1):42-60.
[47]Zhu Kunpeng,Wong Yokesan,Hong Geoksoon.Wavelet analysis of sensor signals for tool condition monitoring:a review and some new results[J].International Journal of Machine Tools and Manufacture,2009,49(7-8):537-553.
[48]李欢欢,司风琪,徐治皋.一种基于鲁棒自联想神经网络的传感器故障诊断方法[J].中国电机 工程学报,2012,32(14)：116-121.
[49]胡明江,王忠,祁利巧,等.柴油机低温启动工况的传感器在线诊断[J].振动、测试与诊断,2010,30(3)：286-290.
[50]陈楚瑶,朱大奇.神经网络主元分析的传感器故障诊断方法[J].系统工程与电子技术,2010,32(7)：1549-1553.
[51]司风琪,李欢欢,徐治皋.基于鲁棒输入训练神经网络的非线性多传感器故障诊断方法及其应用[J].东南大学学报(自然科学版),2011,41(3)：574-578.
[52]吴锋,潘书澜,邹博文.基于BP神经网络的电控汽油机传感器故障在线诊断技术研究[J].内燃机学报,2006,24(4)：369-373.
[53]冯志刚,王祁,徐涛,等.基于小波包和支持向量机的传感器故障诊断方法[J].南京理工大学学报(自然科学版),2008,32(5)：609-615.
[54]冯志刚,王祁,信太克规.基于EMD和SVM的传感器故障诊断方法[J].哈尔滨工业大学学报,2009,41(5)：59-63.
[55]Cheng Junsheng, Yu Dejie, Yang Yu.Application of support vector regression machines to the processing of end effects of Hilbet-Huang transform [J]. Mechanical Systems and Signal Processing,2012,21(3):1197-1211.
[56]Lin Dachao, Guo Zhanglin, An Fengping, et al. Elimination of end effects in empirical mode decomposition by mirror image coupled with support vector regression [J].Mechanical Systems and Signal Processing,2012,31(1):13-18.
[57]Lei Yaguo,Lin Jing,He Zhengjia,et al.A review on ensemble empirical mode decomposition in fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2013,35 (1-2):108-126.
[58]Hsu Chunchin,Chen Muchen,Chen Longsheng.Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring[J].Expert Systems with Applications,2010,37(4): 3264-3273.
[59]Xiao Yingqun, Feng Lianggui.A novel linear ridgelet network approach for analog fault diagnosis using wavelet-based fractal analysis and kernel PCA as preprocessors [J]. Measurement,2012,45(3):297-310.
[60]Xiao Yingqun, He Yigang.A novel approach for analog fault diagnosis based on neural network and improved kernel PCA [J].Neurocomputing,2011,74(7):1102-1115.
[61]Zhu Daqi, Bai Jie, Yang Simon X.A multi-fault diagnosis method for sensor systems based on principle component analysis [J].Sensors,2010,10:241-253.
[62]Sun Weixiang, Chen Jin, Li Jiangqing.Decision tree and PCA-based fault diagnosis of rotating machinery [J].Mechanical Systems and Signal Processing,2007,21(3):1300-1317.
[63]Yu Jianbo.Local and global principal component analysis for process monitoring [J].Journal of Process Control,2012,22(7):1358-1373.
[64]Issam Ben Khediri,Mohamed Limam,Claus Weihs.Wariable window adaptive kernel principal component analysis for nonlinear nonstationary process monitoring[J].Computers&Industrial Engineering,2011,61 (3):437-446.
[65]M.A.Bin Shams, H.M. Budman, T.A.Duever.Fault detection, identification and diagnosis using CUSUM based PCA [J].Chemical Engineering Science,2011,66(20):4488-4498.
[66]沈路.数学形态学在机械故障诊断中的应用研究[D].杭州：浙江大学,2010.
[67]林勇,周晓军,张文斌,等.基于形态小波理论和双谱分析的滚动轴承故障诊断[J].浙江大学学报(工学版),2010,44(3)：432-439.
[68]赵静,何正友,钱清泉.利用广义形态滤波与差分熵的电能质量扰动检测[J].中国电机工程学报,2009,29(7)：121-127.
[69]岳蔚,刘沛.基于数学形态学消噪的电能质量扰动检测方法[J].电力系统自动化,2002,(26)7：13-17.
[70]张立国,杨瑾,李晶,等.基于小波包和数学形态学结合的图像特征提取方法[J].仪器仪表学报,2010,31(10)：2285-2290.
[71]陈维荣,冯倩,张健,等.受电弓滑板状态监测的图像目标提取[J].西南交通大学学报,2010,45(1)：59-64.
[72]申俊琦,胡绳荪,冯胜强,等.基于数学形态学的寒风图像边缘提取[J].天津大学学报,2010,43(4)：373-377.
[73]王耀琦,王晓鹏,张忠林.一种低信噪比环境下的基音检测方法[J].铁道学报,2012,34(2)：58-62.
[74]Zhang Jun,Wang Heping.A new approach of pitch detection based on morphology filter and wavelet transform[C].2010 2th International Workshop on Education Technology and Computer Science,Wuhan,2010:751-753.
[75]李天云,杨梅,周喜超,等.基于小波变换和数学形态学的局部放电信号分析方法[J].电网技术,2007,31(6)：56-60.
[76]龚炜,石青云,程民德.数字空间的数学形态学-理论及应用[M].北京：科学出版社,1997.
[77]张文斌,杨辰龙,周晓军.形态滤波方法在振动信号降噪中的应用[J].浙江大学学报(工学版),2009,43(11)：2096-2099.
[78]李天云,郭跃霞,王静,等.基于数学形态学和短窗功率算法的电能质量扰动检测方法[J].电力自动化设备,2008,28(7)：37-40.
[79]胡爱军,唐贵基,安连锁.基于数学形态学的旋转机械振动信号降噪方法[J].机械工程学报,2006,42(4)：127-130.
[80]安源,田录林,刘家军.基于形态学-小波识别变压器励磁涌流[J].电力自动化设备,2008,28(7)：41-45.
[81]Maragos P, Schafer R. Morphological Filters. Part Ⅰ:Their Set-theoretic Analysis and Relation to Linear Shift-invariant Filters [J]. IEEE Transactions on ASSP,1987,35 (8):1153-1169.
[82]Maragos P,Schafer R.Morphological Filters. Part Ⅱ:Their Relation to Median, Order-statistic, and Stack Filters [J].IEEE Transactions on ASSP,1987,35(8):1170-1184.
[83]Stevenson R.L, Arce G. Morphological filters:statistical and further syntactic properties [J]. IEEE Transactions on Circuits and Systems,1987,34(11):1292-1305.
[84]司大军,束洪春,陈学允,等.输电线路雷击的电磁暂态特征分析及其识别方法研究[J].中国电机工程学报,2005,25(7)：64-69.
[85]张全明,刘会金,周新启,等.电能质量信号的小波软阈值去噪方法[J].高电压技术,2006,32(1)：99-101.
[86]程正兴,杨守志,冯晓霞.小波分析的理论算法进展和应用[M].北京：国防工业出版社,2007.
[87]王慧琴.小波分析与应用[M].北京：北京邮电大学出版社,2011.
[88]张彬,杨风暴.小波分析方法与其应用[M].北京：国防工业出版社,2011.
[89]Biswaranjan Behera.Wavelet packets associated with nonuniform multiresolution analyses [J].Journal of Mathematical Analysis and Applications,2007,328(2):1237-1246.
[90]王旭红,何怡刚.基于小波包和Elman神经网络的异步电机转子断条故障诊断方法[J].湖南大学学报(自然科学版),2010,37(5)：45-48.
[91]Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for non linear non-stationary time series analysis[J]. Proceedings of the Royal Society,1998, 454(1971):903-995.
[92]Wu Zhaohua, Huang Norden.A study of the characteristics of white noise using the empirical mode decomposition method [J].Proceddings:Mathematical Physical and Engineering Science, 2004,460(2046):1597-1611.
[93]Wu Zhaohua, Huang Norden E.Ensemble empirical mode decomposition:a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis,2009,1(1):1-41.
[94]陈略,訾艳阳,何正嘉,等.总体平均经验模式分解与1.5维谱方法的研究[J].西安交通大学学报,2009,43(5)：94-98.
[95]蒋少华,桂卫华,杨春华,等.基于RS与LSSVM多分类法的故障诊断方法及其应用[J].中南大学学报(自然科学版),2009,40(2)：447-451.
[96]Suykens J A K, Vandewalle J.Least squares support vector machine classifiers [J]. Neural Processing Letters,1999,9(3):293-300.
[97]Suykens J A K, Lukas L, Vandewalle J.Sparse least squares vector machine classifiers[C]. Proceedings of the European Symposium on Artificial Neural Networks, Bruges Belgium,2000: 37-42.
[98]Lukas L, Suykens J A K, Vandewalle J.Least squares support vector machines classifiers:A multi two-spiral benchmark problem[C].Proceedings of the Indonesian Student Scientific Meeting,Manchester,UK,2001:289-292.
[99]彭文季,罗兴锜,郭鹏程,等.基于最小二乘支持向量机和信息融合技术的水电机组振动故障诊断[J].中国电机工程学报,2007,27(23)：86-92.
[100]Mohammadreza Afshin. Application of least squares support vector machines in medium-term load forecasting [D].Canada:Ryerson University,2007.
[101]王平,刘玉涛.基于GA和LSSVM的时间序列预测[J].华北电力大学学报(自然科学版),2009,36(4)：100-103.
[102]李大川,忻展红.大型呼叫中心人工呼入量的最小二乘支持向量机模型[J].控制理论与应用,2009,26(7)：815-818.
[103]夏克文,董瑶,杜红斌.基于改进PSO算法的LSSVM油层识别模型[J].控制与决策,2007,22(12)：1385-1389.
[104]Liu Qibin,Yang Minlin,Lei Jing,et al.Modeling and optimizing parabolic trough solar collector systems using the least squares support vector machine method[J].Solar Energy,2012, 86(7):1973-1980.
[105]C.M.Vong, P.K.Wong.Engine ignition signal diagnosis with wavelet packet transforms and multi-class least squares support vector machines [J].Expert Systems with Applications,2011, 38(7):8563-8570.
[106]X.C.Guo, J.H.Yang, C.GWu, et al.A novel LSSVM hyper-parameter selection based on particle swarm optimization [J].Neurocomputing,2008,71(16-18):3211-3215.
[107]Suyens.J.A.K, Van Gestel.T, De Brabanter.J, et al.Least Squares Support Vector Manchine [M]. Singapore:World Scientific Publishing,2002.
[108]Reich Y, Barai S.V.Evaluating machine learning models for engineering problems [J].Artifitial Intelligence in Engineering,1999,13(3):257-272.
[109]S. Sathiya Keerthi.Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms[J].IEEE Transactions on Neural Networks,2002,13(5):1225-1229.
[110]Olivier Chapelle.Alexander Zien. Semi-supervised classification by low density separation[C]. Proceedings of the 10th International Workshop on Artificial Telligence and Statistics, Hastings, Christ Church, Barbados,2005:57-64.
[111]李娜,杨恒,屈志坚,等.基于改进最小二乘支持向量机的电力机车牵引电机建模[J].铁道学报,2011,33(3)：23-27.
[112]李锋,汤宝平,刘文艺.遗传算法优化最小二乘支持向量机的故障诊断[J].重庆大学学报,2010,33(12)：14-20.
[113]隋文涛,路长厚,’Wilson Wang,等.基于模拟退火与LSSVM的轴承故障诊断[J].振动、测试与诊断,2010,30(2)：119-122.
[114]耿立艳,张天伟,赵鹏.基于灰色关联分析的LSSVM铁路货运量预测[J].铁道学报,2012,34(3)：1-6.
[115]龙文,梁昔明,龙祖强.基于混合PSO优化的LSSVM锅炉烟气含氧量预测控制[J].中南大学学报(自然科学版),2012,43(3)：980-985.
[116]黄英杰,姚锡凡,古耀达.基于熵的混合粒子群算法在柔性调度中的应用[J].湖南大学学报(自然科学版),2012,39(3)：48-52.
[117]HSU.C, Lin C.A comparison of methods for multiclass support vector machines [J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[118]T. G. Dietterich, GBakiri. Solving multiclass learning problems via error-correcting output codes [J].Journal of Artificial Intelligence Research,1995,2(1):263-286.
[119]Erin L. Allwein, Robert E. Schapire, Yoram Singer. Reducing multiclass to binary:a unifying approach for margin classifiers [J]. Journal of Machine Learning Research,2000,1:113-141.
[120]邱绵浩,王自营,安钢,等.基于核主元分析与纠错输出编码SVM的齿轮故障诊断[J].振动与冲击,2009,28(5)：1-5.
[121]Platt John C.,Cristianini Nello,Shawe-Taylor John.Large margin DAGs for multiclass classification[C].Proceddings of Neural Information Processing System, Massachusetts:MIT Press,2000:547-553.
[122]易辉,宋晓峰,姜斌,等.基于结点优化的决策导向无环图支持向量机及其在故障诊断中的应用[J].自动化学报,2010,36(3)：427-432.
[123]王艳,陈欢欢,沈毅.有向无环图的多类支持向量机分类算法[J].电机与控制学报,2011,15(4)：85-89.
[124]Hsu Chinwei, Lin Chinjen. A comparison of methods for multi-class support vector machines [J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[125]Yang Chihcheng, Lee Wanjui, Lee Shiejue. Learning of kernel functions in support vector machines[C].2006 International Joint Conference on Neural Networks, Vancouver, Canada, 2006:1150-1155.
[126]杨琳,王从庆,缪鹏,等.基于Huffman最优二叉树支持向量机的舱音记录器背景信号识别[J].宇航学报,2011,32(6)：1428-1434.
[127]崔江,王友仁.模拟电路故障的一种聚类二叉树支持向量机诊断新方法[J].仪器仪表学报,2008,29(10)：2047-2051.
[128]薛宁静.多类支持向量机分类器对比研究[J].计算机工程与设计,2011,32(5)：1792-1795.
[129]Yuan Haiying, Chen Guangju, Xie Yongle.Feature evaluation and extraction based on neural network in analog circuit fault diagnosis [J].Journal of Systems Engineering and Electronics. 2007,18(2):434-436.
[130]Long Bing,Huang Jianguo,Tian Shulin.Least squares support vector machine based on analog-circuit fault diagnosis using wavelet transform preprocessor[C]. International Conference on Communications,Circuits and Systems,Fujian,2008:1026-1029.
[131]Mehran Aminian, Farzan Aminian. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor [J]. IEEE Transactions on Circuits and Systems Ⅱ,2000, 47(2):151-156.
[132]Robert Spina, Shambhu Upadhyaya. Linear circuit fault diagnosis using neuromorphic analyzers [J]. IEEE Transactions on Circuits and Systems Ⅱ,1997,44(3):188-196.
[133]Farzan Aminian,Mehran Aminian,H. W. Collins. Analog fault diagnosis of actual circuits using neural networks[J]. IEEE Transactions on Instrumentation and Measurement,2002,51(3): 544-550.
[134]王佩丽,彭敏放,杨易旻,等.应用模糊最优小波包和LSSVM的模拟电路诊断[J].仪器仪表学报,2010,31(6)：1282-1288.
[135]孙永奎,陈光聥,李辉.基于自适应小波分解和SVM的模拟电路故障诊断[J].仪器仪表学报,2008,29(10)：2105-2109.
[136]马超,陈西宏,徐宇亮,等.广义邻域粗集下的集成特征选择及其选择性集成算法[J].西安交通大学学报,2011,45(6)：34-39.
[137]鲁昌华,汪涌,王玲飞.基于PCA和SVM的模拟电路故障诊断[J].电子测量与仪器学报,2006,22(3)：64-68.
[138]内田清五,陈贺,李毅,等.日本新干线列车制动系统[M].北京：中国铁道出版社,2004.
[139]饶忠,彭俊彬.列车制动[M](第二版).北京：中国铁道出版社,2010.
[140]Zhang Jianqiu, Yan Yong. A wavelet-based approach to abrupt fault detection and diagnosis of sensor [J]. IEEE Transactions on Instrumentation and Measurement,2001,50(5):1389-1396.
[141]Zhu Yonghua,Jin Xinqiao,Du Zhimin.Fault diagnosis for sensors in air handling based on neural network pre-processed by wavelet and fractal [J].Energy and Buildings,2012,44:7-16.
[142]徐涛,王祁.基于小波包神经网络的传感器故障诊断方法[J].传感技术学报,2006,19(4)：1060-1064.
[143]张超,陈建军,郭迅.基于EEMD能量熵和支持向量机的齿轮故障诊断方法[J].中南大学学报(自然科学版),2012,43(3)：932-939.
[144]Vapnik V.An overview of statistical learning theory [J].IEEE Transaction on Neural Networks, 1999,10(5):988-999.
[145]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1)：32-42.
[146]Zhang Sheng, Liu Jian, Tian Jinwen. An SVM-based small target segmentation and clustering approach[C].Proceedings of the 3rd International Conference on Machine Leaning and Cybernetics, Shanghai,2004:3318-3323.
[147]Smits G. F, Jordan E. M. Improved SVM regeression using mixtures of kernels [C]. IEEE Proceedings of the 2002 International Joint Conference on Neural Networks, Honolulu, 2002:2785-2790.
[148]M.Elangovan, V.Sugumaran, K.I.Ramachandran, S.Ravikumar.Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool [J].Expert Systems with Applications,2011,38(12):15202-15207.
[149]M.Zavar, S.Rahati, M.R.Akbarzadeh, et al.Evolutionary model selection in a waelet based support vector machine for automated seizure detection [J].Expert Systems with Application, 2011,38(9):10751-10758.
[150]郑志成,徐卫亚,徐飞,等.基于混合核函数PSO-LSSVM的边坡变形预测[J].岩土力学,2012,33(5)：1421-1427.
[151]Kennedy J, Eberhart.R.C.Particle swarm optimization[C].Proceedings IEEE International Conference Neural Networks, NJ:IEEE Press,1995:1942-1948.
[152]Kennedy.J, Eberhart.R.C.A discrete binary version of the particle swarm algorithm[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, NJ:IEEE Press,1997:4104-4108.
[153]James Kennedy, Russell C.Eberhart, Shi Yuhui.Swarm Intelligence [M].San Francisco, USA: Morgan Kaufmann,2001.
[154]Shi Yuhui Eberhart R. A modified particle swarm optimizer[C]. Proceedings of IEEE International Conference on Evolutionary Computation, Piscataway, NJ:IEEE Press,1998: 69-73.
[155]Shi Yuhui, Russell C. Eberhart. Empirical study of particle swarm optimization [C]. Proceedings of Congress on Evolutionary Computation, Piscataway, NJ:IEEE Press,1999: 1945-1950.
[156]Russell C. Eberhart, Shi Yuhui.Comparing inertia weights and constriction factors in particle swarm optimization[C].Proceedings of the Congress on Evolutionary Computing, NJ:IEEE Service Center,2000:84-88.
[157]魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究[D].太原：中北大学,2009.
[158]杨维,李岐强.粒子群优化算法综述[J].中国工程科学,2004,(6)：87-94.
[159]G.F.Smits, E.M.Jordaan.Improved SVM regression using mixtures of kernels [C].Proceedings of the 2002 International Jiont Conference on Neural Networks, NJ:IEEE Service Center, 2002:2785-2790.
[160]Gao Ying, Xie Shengli. Chaos particle swarm optimization algorithm [J]. Computer Science, 2004,31(8):13-15.
[161]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002.
[162]Tang Xianlun, Zhuang Ling, Cai Jun, et al. Multi-fault classification based on support vector machine trained by chaos particle swarm optimization [J]. Knowledg -Based Systems,2010, 23(5):486-490.
[163]Liang Wei, Zhang Laibin, Wang Mingda. The chaos differential evolution optimization algorithm and its application to support vector regression machine [J]. Journal of Software, 2011,6(2):1297-1304.
[164]Zhao Chenglin, Sun Xuebin, Sun Songlin, et al. Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine[J], Expert Systems with Applications,2011,38(8):9908-9912.
[165]杜文莉,张海龙,钱锋.融合和声搜索的混沌粒子群优化算法及工业应用[J].清华大学学报(自然科学版),2012,52(3)：325-330.
[166]Jiang Huimin, C.K.Kwong, Chen Zengqiang, et al.Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control [J].Expert Systems with Applications,2012,39(1):194-201.
[167]Niu Ben, Zhu Yunlong, He Xiaoxian, et al. MCPSO:A multi-swarm cooperative particle swarm optimizer [J].Applied Mathematics and Computation,2007,185(2):1050-1062.
[168]Niu Ben, Zhu Yunlong, He Xiaoxian, et al. A multi-swarm optimizer based fuzzy mpdeling approach for dynamic systems processing [J].Neurocomputing,2008,71 (7-9):1436-1448.
[169]牛奔,李丽.基于MCPSO算法的BP神经网络训练[J].深圳大学学报理工版,2009,26(2)：147-150.
[170]李丽,牛奔.粒子群优化算法[M].北京：冶金工业出版社,2009.
[171]赵云涛,王京,宋勇,等.具有转换函数的均匀差分进化算法及其性能分析[J].控制理论与应用,2009,26(9)：1014-1018.
[172]纪震,廖惠连,吴青华.粒子群算法及应用[M].北京：科学出版社,2009.
[173]郭军,刘和平,刘平.基于大电流检测的霍尔传感器应用[J].传感器与微系统,2011,30(3)：142-145.