艾萨铜熔炼配料优化及状态控制参数预测方法研究
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摘要
艾萨炉是一种结构紧凑、反应速度快、适应性强、熔炼效率高、二次能源利用好、符合环保要求的世界上较为先进的熔炼炉。艾萨铜熔炼过程是非常复杂的高温、多相的物理化学变化过程,具有多变量、非线性、强耦合、不确定等特点。配料是艾萨铜熔炼过程的紧前工序,进行配料优化获得最佳配料比,对与配料密切相关并反映炉况和产品质量的关键状态参数难以进行检测和优化,由配料和炉况变化引起的关键设备故障状态等难以预测。为了解决上述问题,实现艾萨铜熔炼过程节能降耗、提高资源和熔炼设备利用率及充分发挥熔炼过程生产潜力、提高熔炼过程的技术经济指标及工艺水平,实现企业的可持续发展,本文将结合艾萨炉的特点,围绕铜熔炼过程的配料优化,冰铜温度、冰铜品位和渣中铁硅比三个主要控制参数软测量、艾萨炉故障状态预测等关键问题开展研究。主要完成了以下研究工作：
(1)针对艾萨铜熔炼过程配料优化问题,提出了基于自适应蚁群算法的艾萨铜熔炼过程配料智能优化方法,首先分析了艾萨铜熔炼过程中工艺配料特点,以成本为优化目标,综合考虑工艺、质量、库存等多约束条件,采用自适应蚁群学习算法,将配料优化问题转化为在各种约束条件下的学习建模问题,借助历史配料数据进行建模,实现配料预测与优化。艾萨铜熔炼配料实验结果表明,提出的方法能有效降低生产成本,提高配料系统的效率。
(2)针对艾萨铜熔炼过程控制中冰铜品位、冰铜温度、渣中铁硅比三大参数检测时存在成本高、滞后大、实现困难等问题,提出了一种基于广义最大熵回归的自适应艾萨铜熔炼过程三大参数软测量方法。首先基于核聚类的局部线性嵌入算法对熔炼过程的输入数据进行降维预处理,然后利用隐马尔科夫模型对工况进行检测,最后结合工况检测模型建立广义最大熵自适应模型。实验表明,提出的方法不仅能明显改善误差,而且测量稳定性得到提高,能为实际生产提供有益的指导。
(3)针对艾萨铜熔炼过程中由于配料和炉况波动引起关键设备发生故障,而熔池搅拌剧烈和不可视性造成炉体故障判断困难等问题提出一种融合模糊C均值聚类的特征样本核主元分析和稀疏最小二乘支持向量机(CSKPCA-SLSSVM)的故障监测方法。首先利用模糊C均值算法对采样数据进行聚类,将簇中心样本作为基向量,在此基础上提取出采样数据中的特征样本,并利用核主元分析进行降维预处理,然后基于T2和SPE统计量对艾萨炉故障进行初步识别,最后基于稀疏最小二乘支持向量机故障预测模型对初步识别结果进行细分类。实验结果表明,该方法能能快速反映整个生产过程的变化和故障,帮助监测艾萨炉情况,适合在类似工业过程中推广应用。
ISA furnace is a compact, fast reaction speed, high efficiency, strong adaptability, secondary energy use good, and comply with environmental requirements of the advanced smelting furnace in the world. ISA copper smelting process is very complicated, multi-phase, high temperature physical and chemical change process, with multi-variable, nonlinear, strong coupling, uncertain characteristics. The blending process is the preceding activities of ISA smelting. The optimal proportion of materials is a key of successful smelting, stable conditions of furnace and product quality. Those status parameters of melting bath and key equipment, such as matte grade, matte temperature, a ratio of Fe and SiO2of slag, fault condition of lance and brusque, are always coupled with blending process and difficult to detect and predict. In order to solve these problems, to achieve energy saving of the ISA copper smelting process, improve the utilization of resources and smelting equipment and maximize the production potential of the smelting process, promote the technical and economic index of the smelting process and the level of technology to achieve sustainable development of enterprises, the blending proportion optimization, the soft measurement of three main control parameters, the temperature of the matte, matte grade and ratio of Fe and SiO2of slag, and the prediction of the failure state of the key equipment linked up with the characteristics of the ISA furnace are researched in this thesis. The main research works are as follows:
(1) To be aimed at blending proportion optimization for ISA furnace in copper smelting process, the intelligent optimization method based on adaptive ant colony algorithm is put forward. The first step, based on analyzes the blending process features, objective optimization function with the target at cost, considering the technology, quality, inventory constraints, is established. Then, the optimization problem is transferred into learning modeling in a variety of constraints by using using adaptive ant colony algorithm. At last, the blending proportion optimization is solved based on the modeling with the benefit of historical blending data. The simulating experimental results show that the optimizing method for blending can effectively reduce production costs and improve the efficiency of the blending system.
(2) For ISA furnace copper smelting process, matte grade, the temperature of the matte, ratio of Fe and SiO2of slag are three main controlling parameters. According to high costs， large lag, difficult to measure, checking and controlling these three parameters is difficultl To be aimed at measuring these three key paramters, the soft measurement method is proposed based on generalized maximum entropy regression of the adaptive. At first, the input data of the smelting process are to reduce the dimensionality of pretreatment by locally linear embedding algorithm based on kernel clustering.Then, the operating conditions are detected by using hidden Markov model. Finally the generalized maximum entropy adaptive model is estabilished by combined with the operating condition detection model. The experiments show that the proposed method can significantly improve the error, improved measurement stability, and provide useful guidance for the factory production.
(3) As a result of fluctuations of blending and furnace conditions irregularly, copper smelting is always accompanied by the fault of key equipment of ISA furnace. Due to melting bath stired strongly and invisibly, the faults are difficult to find and predict. To be aimed at fault monitoring and predicting, the method is proposed, which is based on kernel principal component analysis with a fusion of fuzzy C-means clustering feature samples and sparse least squares support vector machine (CSKPCA-SLSSVM). First, by using of Fuzzy C-Means algorithm to cluster the sampled data, the cluster center of the sample is formed as a base vector. Then based on the extracted characteristics of the sample data, the dimensionality of pretreatment is reduced by kernel principal component analysis. At last, based on T2and SPE statistics of ISA furnace failure to initial recognition, the final preliminary recognition results are classified and predicted accurately based on sparse least squares support vector machine fault diagnosis model. The experimental results show that this method can quickly show the change and the failure of the entire production process, contribute to monitoring of ISA furnace, and help to promote the monitoring level of similar industrial processes.
(1)针对艾萨铜熔炼过程配料优化问题,提出了基于自适应蚁群算法的艾萨铜熔炼过程配料智能优化方法,首先分析了艾萨铜熔炼过程中工艺配料特点,以成本为优化目标,综合考虑工艺、质量、库存等多约束条件,采用自适应蚁群学习算法,将配料优化问题转化为在各种约束条件下的学习建模问题,借助历史配料数据进行建模,实现配料预测与优化。艾萨铜熔炼配料实验结果表明,提出的方法能有效降低生产成本,提高配料系统的效率。
(2)针对艾萨铜熔炼过程控制中冰铜品位、冰铜温度、渣中铁硅比三大参数检测时存在成本高、滞后大、实现困难等问题,提出了一种基于广义最大熵回归的自适应艾萨铜熔炼过程三大参数软测量方法。首先基于核聚类的局部线性嵌入算法对熔炼过程的输入数据进行降维预处理,然后利用隐马尔科夫模型对工况进行检测,最后结合工况检测模型建立广义最大熵自适应模型。实验表明,提出的方法不仅能明显改善误差,而且测量稳定性得到提高,能为实际生产提供有益的指导。
(3)针对艾萨铜熔炼过程中由于配料和炉况波动引起关键设备发生故障,而熔池搅拌剧烈和不可视性造成炉体故障判断困难等问题提出一种融合模糊C均值聚类的特征样本核主元分析和稀疏最小二乘支持向量机(CSKPCA-SLSSVM)的故障监测方法。首先利用模糊C均值算法对采样数据进行聚类,将簇中心样本作为基向量,在此基础上提取出采样数据中的特征样本,并利用核主元分析进行降维预处理,然后基于T2和SPE统计量对艾萨炉故障进行初步识别,最后基于稀疏最小二乘支持向量机故障预测模型对初步识别结果进行细分类。实验结果表明,该方法能能快速反映整个生产过程的变化和故障,帮助监测艾萨炉情况,适合在类似工业过程中推广应用。
ISA furnace is a compact, fast reaction speed, high efficiency, strong adaptability, secondary energy use good, and comply with environmental requirements of the advanced smelting furnace in the world. ISA copper smelting process is very complicated, multi-phase, high temperature physical and chemical change process, with multi-variable, nonlinear, strong coupling, uncertain characteristics. The blending process is the preceding activities of ISA smelting. The optimal proportion of materials is a key of successful smelting, stable conditions of furnace and product quality. Those status parameters of melting bath and key equipment, such as matte grade, matte temperature, a ratio of Fe and SiO2of slag, fault condition of lance and brusque, are always coupled with blending process and difficult to detect and predict. In order to solve these problems, to achieve energy saving of the ISA copper smelting process, improve the utilization of resources and smelting equipment and maximize the production potential of the smelting process, promote the technical and economic index of the smelting process and the level of technology to achieve sustainable development of enterprises, the blending proportion optimization, the soft measurement of three main control parameters, the temperature of the matte, matte grade and ratio of Fe and SiO2of slag, and the prediction of the failure state of the key equipment linked up with the characteristics of the ISA furnace are researched in this thesis. The main research works are as follows:
(1) To be aimed at blending proportion optimization for ISA furnace in copper smelting process, the intelligent optimization method based on adaptive ant colony algorithm is put forward. The first step, based on analyzes the blending process features, objective optimization function with the target at cost, considering the technology, quality, inventory constraints, is established. Then, the optimization problem is transferred into learning modeling in a variety of constraints by using using adaptive ant colony algorithm. At last, the blending proportion optimization is solved based on the modeling with the benefit of historical blending data. The simulating experimental results show that the optimizing method for blending can effectively reduce production costs and improve the efficiency of the blending system.
(2) For ISA furnace copper smelting process, matte grade, the temperature of the matte, ratio of Fe and SiO2of slag are three main controlling parameters. According to high costs， large lag, difficult to measure, checking and controlling these three parameters is difficultl To be aimed at measuring these three key paramters, the soft measurement method is proposed based on generalized maximum entropy regression of the adaptive. At first, the input data of the smelting process are to reduce the dimensionality of pretreatment by locally linear embedding algorithm based on kernel clustering.Then, the operating conditions are detected by using hidden Markov model. Finally the generalized maximum entropy adaptive model is estabilished by combined with the operating condition detection model. The experiments show that the proposed method can significantly improve the error, improved measurement stability, and provide useful guidance for the factory production.
(3) As a result of fluctuations of blending and furnace conditions irregularly, copper smelting is always accompanied by the fault of key equipment of ISA furnace. Due to melting bath stired strongly and invisibly, the faults are difficult to find and predict. To be aimed at fault monitoring and predicting, the method is proposed, which is based on kernel principal component analysis with a fusion of fuzzy C-means clustering feature samples and sparse least squares support vector machine (CSKPCA-SLSSVM). First, by using of Fuzzy C-Means algorithm to cluster the sampled data, the cluster center of the sample is formed as a base vector. Then based on the extracted characteristics of the sample data, the dimensionality of pretreatment is reduced by kernel principal component analysis. At last, based on T2and SPE statistics of ISA furnace failure to initial recognition, the final preliminary recognition results are classified and predicted accurately based on sparse least squares support vector machine fault diagnosis model. The experimental results show that this method can quickly show the change and the failure of the entire production process, contribute to monitoring of ISA furnace, and help to promote the monitoring level of similar industrial processes.
引文
[1]朱祖泽,贺家齐.现代铜冶金学[M].北京：科学出版社,2003：273-295.
[2]史谊峰.云铜艾萨熔炼炉—中国冶炼厂的成功改造[C]//中国有色金属学会第七届学术年会论文集.北京：冶金工业出版社,2008：403-407.
[3]BERGH L G, YIANATOS J B. Current status and limitations of copper SX/EW plants control[J]. Minerals Engineerin,2001,14 (9):975-985.
[4]朱卫平.21世纪的炼铜业[J].中国有色冶金,2004,2(1)：45-50.
[5]PLAYER R L. Copper Isasmelt process investigation[C]//The Howard Worner International Symposium on Injection in Pyrometallurgy. Melbourne:TMS Publishing Company,1996:439-446.
[6]陈宝光摘译.菲尔普斯道奇迈阿密公司铜冶炼厂艾萨炉的操作[J].有色冶炼,2002,31(2)：45-48.
[7]阳春华,王晓丽,陶杰等.铜闪速熔炼配料过程建模与智能优化方法研究[J].系统仿真学报,2008,20(8)：2152-2155..
[8]阳春华,段小刚,王雅琳等.烧结法生产氧化铝生料浆的配料专家系统设计[J].中南大学学报(自然科学版),2005,36(4)：648-652.
[9]吕学伟,白晨光,邱贵宝.基于遗传算法的烧结配料综合优化研究[J].钢铁,2007,42(4)：12-15,20.
[10]吴敏,王春生,曹卫华.基于预测模型与调整规则的烧结配料优化综合集成方法[J].系统仿真学报,2008,20(9)：2423-2428.
[11]王炜,陈畏林,贾斌等.基于线性规划和神经网络的优化烧结配料系统开发[J].烧结球团,2006,31(1)：27-30.
[12]白锐,柴天佑.基于PSO算法的生料浆配料过程的优化控制[J].控制工程,2009,16(1)：76-79.
[13]喻寿益,王吉林,彭晓波.基于神经网络的铜闪速熔炼过程工艺参数预测模型[J].中南大学学报(自然科学版),2007,38(3)：523-527.
[14]李琦.炼油精馏过程软测量与综合优化控制的研究[D].大连：大连理工大学,2008.
[15]陈晓方,桂卫华,王雅琳.基于智能集成策略的烧结块残硫软测量模型[J].控制理论与应 用,2004,21(1)：75-80.
[16]颜青君,桂卫华,彭晓波.基于智能集成策略的铜闪速炉冰铜温度预测模型[J].计算机测量与控制,2007,15(9)：1157-1160.
[17]汪金良,卢宏,曾青云.GA-BP算法及其在冰铜品位神经网络模型中的应用,江西有色金属,2003,17(3)：39-41.
[18]LAURI D, ROSSITER J A, SANCHIS J, et al. Data-driven latent-variable model-based predictive control for continuous processes [J]. Journal of Process Control,2010,20 (10): 1207-1219.
[19]桂卫华,阳春华,李勇刚.基于数据驱动的铜闪速熔炼过程操作模式优化及应用[J].自动化学报,2009,35(6)：717-724.
[20]VENKAT V, RAGHUNATHAN R, SURYA N K, et al. Areview of processfaultdetection and diagnosis:Part III:Process history based methods[J]. Computers & Chemical Engineering,2003,27(3):327-346.
[21]PHILIP T. Process control in metallurgical plants—from an Xstrata perspective[J]. Annual Reviews in Control,2007,31(2):221-239.
[22]蒋少华,桂卫华,阳春华,唐朝晖.基于核主元分析与支持向量机的监测诊断方法及其应用.中南大学学报(自然科学版),2009,40(5)：1323-1328.
[23]张曦,陈世和,朱亚清,阎威武.基于KPCR的发电机组参数预测与估计[J].电力自动化设备,2010,30(10)：54-57.
[24]汤健,赵立杰,岳恒,柴天佑.基于多源数据特征融合的球磨机负荷软测量.浙江大学学报,2010,44(7)：1406-1413.
[25]NAZATUL A A M, MARK P T, JOHN J J C, et al. Multivariate statistical monitoring of the aluminium smelting process[J]. Computers and Chemical Engineering,2011, (35):2457-2468.
[26]邓晓刚,田学民.基于非线性主元子空间的故障模式识别方法[J].系统仿真学报,2009,21(2)：478-481.
[27]姜万录,吴胜强,刘思远.指数加权动态核主元分析法及其在故障诊断中应用[J].机械工程学报,2011,47(3)：63-68.
[28]王心怡,李志农,张新广,袁振伟.基于稀化核函数主元分析的机械故障诊断方法研究[J].机械强度,2009,31(5)：751-754.
[29]赵忠盖,刘飞.基于稀疏核主元分析的在线非线性过程监测[J].化工学报,2008,59(7)：1773-1777.
[30]王新峰,邱静,刘冠军.基于有监督核函数主元分析的故障状态识别[J].测试技术学报,2005,19(2)：200-203.
[31]EBERHART R C, SHI Y. Particle swarm optimization:developments, applications and resources[C]//Proceedings of the IEEE Congress on Evolutionary Computation. Piscataway: IEEE Service Center,2001:81-86.
[32]RAVINDRA K A, JAMES B O, ASHISH T. A greedy genetic algorithm for the quadratic assignment problem[J]. Computers & Operations Research,2000,27:917-93.
[33]FAN X H, WANG H D. Expert system for sinter chemical composition control based on adaptive prediction[J]. Transations of Nonferros Metal Society of China,1996,6(1):47-50.
[34]DANTZIG G B. Linear programming[J]. Informs,2002,50(1):42-47.
[35]COLORNI A, DORIGO M, MANIEZZO V, et al. Ant system for job shop scheduling [J]. Belgian J. of Operations Research Statistics and Computer Science,1994,34(1):39-53.
[36]BRINKLEY S R. Calculation of the equilibrium composition of systems of many constituents [J]. Journal of Chemical Physics,1947,15(1):107-110.
[37]高倩,苏宏业,刘瑞兰等.基于PLS软测量技术在加氢裂化产品质量控制中的应用[J].上海交通大学学报,2002,36(z1)：135-137.
[38]SU H B, FAN L T, JOHN R S. Monitoring the process of epoxy/graphite fiber composites with a recurrent neural network as a soft sensor[J]. Engineering Application of Artificial Intelligence,1998,11(2):293-306.
[39]彭晓波,桂卫华,李勇刚等.基于动态T-S递归模糊神经网络的闪速熔炼过程参数软测量[J].仪器仪表学报.2008,29(10)：2029-2033.
[40]袁平,毛志忠,王福利.基于多支持向量机的软测量模型[J].系统仿真学报.2006,18(6)：1458-1464,1465.
[41]YU D. Fault diagnosis for hydraulic drive system using a parameter estimation method[J]. Control Engineering Practice,1997,5(9):1283-1291.
[42]MAGNI J F, MOUYON P. On residual generation by observer and parity space approaches[J]. IEEE Transactions on Automatic Control,1994,39(2):441-447.
[43]FENG D, CHENG T W. Parameter estimation of dual rate stochastic systems by using an output error method [J]. IEEE Transactions on Automatic Control,2005,50(9):1436-1441.
[44]施金良,贾碧,余群威等.人工智能高炉冶炼过程专家系统[J].重庆大学学报(自然科学版),2005,28(5)；63-67,85.
[45]CHENG Y S, MELHEM H G. Monitoring bridge health using fuzzy case-based reasoning[J]. Advanced in Engineering Informatics,2005,19(4):299-315.
[46]贾明兴,王福利,郭小萍.改进PCA及其在真空自耗炉故障诊断中的应用[J].东北大学学报(自然科学版),2007,28(9)：1221-1224.
[47]杨帆,唐朝晖,桂卫华.PCA过程监测方法在密闭鼓风炉铅锌熔炼中的应用[J].计算机测量与控制,2007,15(90)：1169-1171.
[48]蒋少华,桂卫华,阳春华.基于核主元分析与支持向量机的监控诊断方法及其应用[J].中南大学学报(自然科学版),2009,40(5)：1323-1328.
[49]何宁,谢磊,王树青等.基于独立成分的动态多变量过程的故障检测与诊断方法[J].化工学报,2005,56(4)：646-652.
[50]LEO H C, EVAN L R, RICHARD D B. Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis [J]. Chemometrics and Intelligent Laboratory Systems,2000,50(2):243-252.
[51]陶杰.铜熔炼配料过程满意优化模型研究及应用[D].长沙：中南大学,2007.
[52]王炜,陈畏林等.基于线形规划和神经网络的优化烧结配料系统开发[J].烧结球团,2006,31(1)：27-30.
[53]孔玲爽,阳春华,于雅琳,桂卫华.考虑多性能指标的配料优化模型及求解算法[J].中南大学学报(自然科学版),2010,41(1)：213-218.
[54]王春生.铅锌烧结配料过程的智能集成建模与优化控制策略研究[D].长沙：中南大学,2008.
[55]李云.艾萨炉配料工艺的数学模型[J].昆明理工大学学报,2000,25(5)：80-82.
[56]COLORINI A, DORIGO M, MANIEZZO V, et al. Distributed optimization by ant colonies[C]//Proceedings of European Confront Artificial Life. Paris:Elsevier Publishing, 1991:134-142.
[57]COLORINI A, DORIGO M, MANIEZZO V. An investigation of some properties of an ant algorithm[C]//Proceedings of Parallel Problem Solving from Nature. Brussels:Elsevier Publishing,1992:509-520.
[58]段海滨.蚁群算法原理及其应用[M].北京：科学出版社,2005.
[59]刘红芳,王荣成,徐伟等.蚁群算法在杭钢烧结混匀矿等Si02等TFe配料中的应用[J].烧结球团,2007,32(1)：33-38.
[60]李智,谢兆鸿,姚驻斌.蚁群算法在原料矿粉混匀优化中的应用[J].矿冶,2004,13(2)：75-79.
[61]付江,王华.艾萨熔池熔炼的应用与优化[J].工业加热,2007,36(1)：35-40.
[62]程利平.炉熔炼计算机模拟[D].昆明：昆明理工大学,2001.
[63]师黎,陈铁军,李晓媛、姚利娜.智能控制理论及应用[M].北京：清华大学出版社,2009.
[64]陈峻,沈洁,秦玲,陈宏建.基于分布均匀度的自适应蚁群算法[J].软件学报,2003,14(8)：1379-1387.
[65]SHIMPO R,GOTOS,OGWA,et al.硫化矿冶炼的进展(上册)[M].包晓波译.北京：冶金工业出版社,1990：273-277.
[66]黎书华,梅显芝等.贵溪闪速炉铜锍熔炼过程热力学模型[J].中南工业大学学报,1995,5(2)：627-637.
[67]黄克雄,黎书华等.贵溪闪速炉造锍熔炼过程计算机模拟[J].中南工业大学学报,1996,(2)：173-176.
[68]王吉林.基于质量平衡的铜闪速熔炼过程模型及关键参数预测研究[D].长沙：中南大学,2006.
[69]汪金良,曾青云.应用神经网络技术建立铜闪速熔炼温度模型[J].有色金属,2002(1)：12-14.
[70]万维汉.工业生产过程的产品质量控制以及应用[J].系统仿真学报,2001,13(z1)：152-155.
[71]万维汉,万百五,史维祥等.镍闪速熔炼过程的模糊动态质量模型[J].西安交通大学学报,2000,34(3)：54-59.
[72]刘伯高.化工过程推断估计的若干问题研究[D].上海：华东理工大学,1999.
[73]刘瑞兰,苏宏业,褚健.基于改进模糊神经网络的软测量建模方法[J].信息与控制,2003,32(4)：367-370.
[74]彭晓波.铜闪速熔炼过程智能优化方法及应用[D].博士学位论文.长沙：中南大学.2008.
[75]KANDINER H J, BRINKLEY JR S R. Calculation of complex equilibrium relations[J]. Industrial and Engineering Chemistry,1950,42(5):850-855.
[76]陈诗国,张道强.半监督降维方法的实验比较[J].软件学报,2011,22(1)：28-43..
[77]VAN DER MAATEN L, POSTMA E, VAN DEN HERIK J. Dimension reduction: a comparative review,TiCC-TR 2009-005[R]. Tilburg:Tilburg University,2009.
[78]HOTELLING H. Analysis of a complex of statistical variables into principal components[J]. Journal of Educational Psychology,1933,24(6):417-441.
[79]郑小霞,钱锋.基于PCA和最小二乘支持向量机的软测量建模[J].系统仿真学报,2006,18(3)：739-741.
[80]赵望达,徐志胜,吴敏火.灾模拟实验炉温软测量建模的研究[J].计算机测量与控制,2006,14(10)：1296-1298.
[81]扬尔辅,周强,胡益锋等.基于PCA-RBF神经网络的工业裂解炉收率在线预测软测量方法[J].系统仿真学报.2001,13：194-197.
[82]FISHER R A. The use of multiple measurements in taxonomic problems. Annals of Eugenics,1936,7(2):179-188.
[83]SCHOLKOPF B, SMOLA A J, MULLER K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation,1998,10(5):1299-1319.
[84]YANG J, FRANGI A F, YANG J Y, et al. KPCA plus LDA:a complete kernel fisher discriminant framework for feature extraction and recognition[J]. Pattern Analysis and Machine Intelligence,2005,27(2):230-244.
[85]TENENBAUM J, DE SILVA V, LANGFORD J. A global geometric framework for nonlinear dimensionality reduction. Science,2000,90(5500):2319-2323.
[86]ROWEIS S T, SAUL L K. Nonlnear dimensionality reduction by locally linear embedding. Science,2000,90(5500):2323-2326.
[87]王和勇,郑杰,姚正安.基于聚类和改进距离的LLE方法在数据降维中的应用[J].计算机研究与发展,2006,43(8)：1485-1490.
[88]KANUNGO T, MOUNT D M, NETANYAHU N S, et al. An efficient k-means clustering algorithm:analysis and implementation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
[89]PAL N R, PAL K, KELLER J M, et al. A possibilistic fuzzy c-means clustering algorithm [J]. IEEE Transactions on Fuzzy Systems,2005,13(4):517-530.
[90]高尚,杨静宇.一种新的基于粒子群算法的聚类方法[J].南京航空航天大学学报,2006,38(S1)：62-65.
[91]JAYNES E T. Information theory and statistical mechanics[J]. Physical Review,1957,108: 171-190.
[92]霍映宝,朱长虹,韩之俊.广义最大熵回归分析[J].南京理工大学学报,2006,12(6)：793-796.
[93]RABINER L, JUANG B. An introduction to hidden Markov models[J]. ASSP Magazine, 1986,3(1):4-16.
[94]SVANTE W. Principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems,1987,2(1-3):37-52.
[95]沈欧,李元,吕锋.基于PCA故障诊断的化工过程多智能体优化策略[J].吉林大学学报(工学版),2004,34(z1)：203-206.
[96]汤健,赵立杰,岳恒,等.基于多源数据特征融合的球磨机负荷软测量.浙江大学学报,2010,44(7)：1406-1413.
[97]LEE J M, YOO C K, CHOI S W, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science,2004,59(1):223-234.
[98]KIM S W, OOMMEN B J. On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods[J]. Pattern Recognition,2004,37(2):227-239.
[99]BAUDAT G, ANOUAR F. Feature vector selection and projection using kernels[J]. Neurocomputing,2003,55(1-2):21-38.
[100]SUYKENS J A K, VANDEWALLE J. Least Squares Support Vector Machine Classifiers[J]. Neural Processing Letters,1998,9(3):293-300.
[101]杨奎河,单甘霖,赵玲玲.基于最小二乘支持向量机的汽轮机故障诊断[J].控制与决策,2007,22(7)：778-782.
[102]王剑非,姜斌,冒泽慧.基于LSSVM的卫星姿态控制系统故障诊断[J].控制工程,2008,15(3)：334-336.
[103]安剑奇,吴敏,何勇等.基于料面温度场特征的高炉炉况诊断方法[J].浙江大学学报(工学版),2010,44(7).1276-1281.
[104]余正涛,邹俊杰,赵兴等.基于主动学习的最小二乘支持向量机稀疏化[J].南京理工 大学学报.2012,36(1)：12-17.
[105]SCHOLKOPF B, SMOLA A, MULLER K R. Nolinear component analysis as a kernel eigenvalue problem[J]. Neural Computation,1998,10(5):1299-1319.
[106]DANIAN Z. Reaseach on kernel methods in machine learning[D]. Peking:Tsinghua University,2006.
[107]黄廷祝.块Jacobi迭代阵的收敛性[J].电子科技大学学报,1996,25(6)：663-665.
[108]高海华,杨辉华,王行愚.基于核KPCA特征收取的SVM网络入侵检测方法[J].华东理工大学学报,2006,32(3)：321-326.
[109]赵忠盖,刘飞.基于稀疏核主元分析的在线非线性过程监测[J].化工学报,2008,59(7)：1773-1777.
[110]王新峰,邱静,刘冠军.基于有监督核函数主元分析的故障状态识别[J].测试技术学报,2005,19(2)：200-203.
[111]张亮,黄曙光,郭浩.快速核有监督局部保留投影算法[J].电子与信息学报,2011,33(5)：1049-1054.
[112]PAL N R. A possibilistic fuzzy c-means clustering algorithm[J]. IEEE Transactions on Fuzzy Systems,2005,13(4):508-516.
[113]甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机[J].浙江大学学报(工学版),2007,41(2)：245-248.
[114]WANG J, LU H P, PLATANIOTIS K N, et al. Gaussian kernel optimization for pattern classification[J]. Pattern recognition,2009,42(7):1237-1247.
[115JTAKU K, YUJIN M. Fast Methods for Kernel-based Text Analysis[C]//Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics,2003:24-31.
[116]ZHANG L, ZHOU W D, JIAO J C. Wavelet Support Vector Machine[J]. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS,2004,34(1):34-39.
[117]HSUAN T L, CHIH J L. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods classification[R]. Taipei:National Taiwan University,2003.
[118]张邦琪,朱相.泽.提高云南铜业艾萨炉寿命的实践与探讨[J].有色金属,2005,57(4)：47-53.
[119]姜万录,吴胜强,刘思远.指数加权动态核主元分析法及其在故障诊断中应用[J].机械工程学报,2011,47(3)：63-68.
[120]刘爱伦,袁小艳,俞金寿.基于KPCA-SVC的复杂过程故障诊断[J].仪器仪表学报,2007,28(5)：870-874.
[2]史谊峰.云铜艾萨熔炼炉—中国冶炼厂的成功改造[C]//中国有色金属学会第七届学术年会论文集.北京：冶金工业出版社,2008：403-407.
[3]BERGH L G, YIANATOS J B. Current status and limitations of copper SX/EW plants control[J]. Minerals Engineerin,2001,14 (9):975-985.
[4]朱卫平.21世纪的炼铜业[J].中国有色冶金,2004,2(1)：45-50.
[5]PLAYER R L. Copper Isasmelt process investigation[C]//The Howard Worner International Symposium on Injection in Pyrometallurgy. Melbourne:TMS Publishing Company,1996:439-446.
[6]陈宝光摘译.菲尔普斯道奇迈阿密公司铜冶炼厂艾萨炉的操作[J].有色冶炼,2002,31(2)：45-48.
[7]阳春华,王晓丽,陶杰等.铜闪速熔炼配料过程建模与智能优化方法研究[J].系统仿真学报,2008,20(8)：2152-2155..
[8]阳春华,段小刚,王雅琳等.烧结法生产氧化铝生料浆的配料专家系统设计[J].中南大学学报(自然科学版),2005,36(4)：648-652.
[9]吕学伟,白晨光,邱贵宝.基于遗传算法的烧结配料综合优化研究[J].钢铁,2007,42(4)：12-15,20.
[10]吴敏,王春生,曹卫华.基于预测模型与调整规则的烧结配料优化综合集成方法[J].系统仿真学报,2008,20(9)：2423-2428.
[11]王炜,陈畏林,贾斌等.基于线性规划和神经网络的优化烧结配料系统开发[J].烧结球团,2006,31(1)：27-30.
[12]白锐,柴天佑.基于PSO算法的生料浆配料过程的优化控制[J].控制工程,2009,16(1)：76-79.
[13]喻寿益,王吉林,彭晓波.基于神经网络的铜闪速熔炼过程工艺参数预测模型[J].中南大学学报(自然科学版),2007,38(3)：523-527.
[14]李琦.炼油精馏过程软测量与综合优化控制的研究[D].大连：大连理工大学,2008.
[15]陈晓方,桂卫华,王雅琳.基于智能集成策略的烧结块残硫软测量模型[J].控制理论与应 用,2004,21(1)：75-80.
[16]颜青君,桂卫华,彭晓波.基于智能集成策略的铜闪速炉冰铜温度预测模型[J].计算机测量与控制,2007,15(9)：1157-1160.
[17]汪金良,卢宏,曾青云.GA-BP算法及其在冰铜品位神经网络模型中的应用,江西有色金属,2003,17(3)：39-41.
[18]LAURI D, ROSSITER J A, SANCHIS J, et al. Data-driven latent-variable model-based predictive control for continuous processes [J]. Journal of Process Control,2010,20 (10): 1207-1219.
[19]桂卫华,阳春华,李勇刚.基于数据驱动的铜闪速熔炼过程操作模式优化及应用[J].自动化学报,2009,35(6)：717-724.
[20]VENKAT V, RAGHUNATHAN R, SURYA N K, et al. Areview of processfaultdetection and diagnosis:Part III:Process history based methods[J]. Computers & Chemical Engineering,2003,27(3):327-346.
[21]PHILIP T. Process control in metallurgical plants—from an Xstrata perspective[J]. Annual Reviews in Control,2007,31(2):221-239.
[22]蒋少华,桂卫华,阳春华,唐朝晖.基于核主元分析与支持向量机的监测诊断方法及其应用.中南大学学报(自然科学版),2009,40(5)：1323-1328.
[23]张曦,陈世和,朱亚清,阎威武.基于KPCR的发电机组参数预测与估计[J].电力自动化设备,2010,30(10)：54-57.
[24]汤健,赵立杰,岳恒,柴天佑.基于多源数据特征融合的球磨机负荷软测量.浙江大学学报,2010,44(7)：1406-1413.
[25]NAZATUL A A M, MARK P T, JOHN J J C, et al. Multivariate statistical monitoring of the aluminium smelting process[J]. Computers and Chemical Engineering,2011, (35):2457-2468.
[26]邓晓刚,田学民.基于非线性主元子空间的故障模式识别方法[J].系统仿真学报,2009,21(2)：478-481.
[27]姜万录,吴胜强,刘思远.指数加权动态核主元分析法及其在故障诊断中应用[J].机械工程学报,2011,47(3)：63-68.
[28]王心怡,李志农,张新广,袁振伟.基于稀化核函数主元分析的机械故障诊断方法研究[J].机械强度,2009,31(5)：751-754.
[29]赵忠盖,刘飞.基于稀疏核主元分析的在线非线性过程监测[J].化工学报,2008,59(7)：1773-1777.
[30]王新峰,邱静,刘冠军.基于有监督核函数主元分析的故障状态识别[J].测试技术学报,2005,19(2)：200-203.
[31]EBERHART R C, SHI Y. Particle swarm optimization:developments, applications and resources[C]//Proceedings of the IEEE Congress on Evolutionary Computation. Piscataway: IEEE Service Center,2001:81-86.
[32]RAVINDRA K A, JAMES B O, ASHISH T. A greedy genetic algorithm for the quadratic assignment problem[J]. Computers & Operations Research,2000,27:917-93.
[33]FAN X H, WANG H D. Expert system for sinter chemical composition control based on adaptive prediction[J]. Transations of Nonferros Metal Society of China,1996,6(1):47-50.
[34]DANTZIG G B. Linear programming[J]. Informs,2002,50(1):42-47.
[35]COLORNI A, DORIGO M, MANIEZZO V, et al. Ant system for job shop scheduling [J]. Belgian J. of Operations Research Statistics and Computer Science,1994,34(1):39-53.
[36]BRINKLEY S R. Calculation of the equilibrium composition of systems of many constituents [J]. Journal of Chemical Physics,1947,15(1):107-110.
[37]高倩,苏宏业,刘瑞兰等.基于PLS软测量技术在加氢裂化产品质量控制中的应用[J].上海交通大学学报,2002,36(z1)：135-137.
[38]SU H B, FAN L T, JOHN R S. Monitoring the process of epoxy/graphite fiber composites with a recurrent neural network as a soft sensor[J]. Engineering Application of Artificial Intelligence,1998,11(2):293-306.
[39]彭晓波,桂卫华,李勇刚等.基于动态T-S递归模糊神经网络的闪速熔炼过程参数软测量[J].仪器仪表学报.2008,29(10)：2029-2033.
[40]袁平,毛志忠,王福利.基于多支持向量机的软测量模型[J].系统仿真学报.2006,18(6)：1458-1464,1465.
[41]YU D. Fault diagnosis for hydraulic drive system using a parameter estimation method[J]. Control Engineering Practice,1997,5(9):1283-1291.
[42]MAGNI J F, MOUYON P. On residual generation by observer and parity space approaches[J]. IEEE Transactions on Automatic Control,1994,39(2):441-447.
[43]FENG D, CHENG T W. Parameter estimation of dual rate stochastic systems by using an output error method [J]. IEEE Transactions on Automatic Control,2005,50(9):1436-1441.
[44]施金良,贾碧,余群威等.人工智能高炉冶炼过程专家系统[J].重庆大学学报(自然科学版),2005,28(5)；63-67,85.
[45]CHENG Y S, MELHEM H G. Monitoring bridge health using fuzzy case-based reasoning[J]. Advanced in Engineering Informatics,2005,19(4):299-315.
[46]贾明兴,王福利,郭小萍.改进PCA及其在真空自耗炉故障诊断中的应用[J].东北大学学报(自然科学版),2007,28(9)：1221-1224.
[47]杨帆,唐朝晖,桂卫华.PCA过程监测方法在密闭鼓风炉铅锌熔炼中的应用[J].计算机测量与控制,2007,15(90)：1169-1171.
[48]蒋少华,桂卫华,阳春华.基于核主元分析与支持向量机的监控诊断方法及其应用[J].中南大学学报(自然科学版),2009,40(5)：1323-1328.
[49]何宁,谢磊,王树青等.基于独立成分的动态多变量过程的故障检测与诊断方法[J].化工学报,2005,56(4)：646-652.
[50]LEO H C, EVAN L R, RICHARD D B. Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis [J]. Chemometrics and Intelligent Laboratory Systems,2000,50(2):243-252.
[51]陶杰.铜熔炼配料过程满意优化模型研究及应用[D].长沙：中南大学,2007.
[52]王炜,陈畏林等.基于线形规划和神经网络的优化烧结配料系统开发[J].烧结球团,2006,31(1)：27-30.
[53]孔玲爽,阳春华,于雅琳,桂卫华.考虑多性能指标的配料优化模型及求解算法[J].中南大学学报(自然科学版),2010,41(1)：213-218.
[54]王春生.铅锌烧结配料过程的智能集成建模与优化控制策略研究[D].长沙：中南大学,2008.
[55]李云.艾萨炉配料工艺的数学模型[J].昆明理工大学学报,2000,25(5)：80-82.
[56]COLORINI A, DORIGO M, MANIEZZO V, et al. Distributed optimization by ant colonies[C]//Proceedings of European Confront Artificial Life. Paris:Elsevier Publishing, 1991:134-142.
[57]COLORINI A, DORIGO M, MANIEZZO V. An investigation of some properties of an ant algorithm[C]//Proceedings of Parallel Problem Solving from Nature. Brussels:Elsevier Publishing,1992:509-520.
[58]段海滨.蚁群算法原理及其应用[M].北京：科学出版社,2005.
[59]刘红芳,王荣成,徐伟等.蚁群算法在杭钢烧结混匀矿等Si02等TFe配料中的应用[J].烧结球团,2007,32(1)：33-38.
[60]李智,谢兆鸿,姚驻斌.蚁群算法在原料矿粉混匀优化中的应用[J].矿冶,2004,13(2)：75-79.
[61]付江,王华.艾萨熔池熔炼的应用与优化[J].工业加热,2007,36(1)：35-40.
[62]程利平.炉熔炼计算机模拟[D].昆明：昆明理工大学,2001.
[63]师黎,陈铁军,李晓媛、姚利娜.智能控制理论及应用[M].北京：清华大学出版社,2009.
[64]陈峻,沈洁,秦玲,陈宏建.基于分布均匀度的自适应蚁群算法[J].软件学报,2003,14(8)：1379-1387.
[65]SHIMPO R,GOTOS,OGWA,et al.硫化矿冶炼的进展(上册)[M].包晓波译.北京：冶金工业出版社,1990：273-277.
[66]黎书华,梅显芝等.贵溪闪速炉铜锍熔炼过程热力学模型[J].中南工业大学学报,1995,5(2)：627-637.
[67]黄克雄,黎书华等.贵溪闪速炉造锍熔炼过程计算机模拟[J].中南工业大学学报,1996,(2)：173-176.
[68]王吉林.基于质量平衡的铜闪速熔炼过程模型及关键参数预测研究[D].长沙：中南大学,2006.
[69]汪金良,曾青云.应用神经网络技术建立铜闪速熔炼温度模型[J].有色金属,2002(1)：12-14.
[70]万维汉.工业生产过程的产品质量控制以及应用[J].系统仿真学报,2001,13(z1)：152-155.
[71]万维汉,万百五,史维祥等.镍闪速熔炼过程的模糊动态质量模型[J].西安交通大学学报,2000,34(3)：54-59.
[72]刘伯高.化工过程推断估计的若干问题研究[D].上海：华东理工大学,1999.
[73]刘瑞兰,苏宏业,褚健.基于改进模糊神经网络的软测量建模方法[J].信息与控制,2003,32(4)：367-370.
[74]彭晓波.铜闪速熔炼过程智能优化方法及应用[D].博士学位论文.长沙：中南大学.2008.
[75]KANDINER H J, BRINKLEY JR S R. Calculation of complex equilibrium relations[J]. Industrial and Engineering Chemistry,1950,42(5):850-855.
[76]陈诗国,张道强.半监督降维方法的实验比较[J].软件学报,2011,22(1)：28-43..
[77]VAN DER MAATEN L, POSTMA E, VAN DEN HERIK J. Dimension reduction: a comparative review,TiCC-TR 2009-005[R]. Tilburg:Tilburg University,2009.
[78]HOTELLING H. Analysis of a complex of statistical variables into principal components[J]. Journal of Educational Psychology,1933,24(6):417-441.
[79]郑小霞,钱锋.基于PCA和最小二乘支持向量机的软测量建模[J].系统仿真学报,2006,18(3)：739-741.
[80]赵望达,徐志胜,吴敏火.灾模拟实验炉温软测量建模的研究[J].计算机测量与控制,2006,14(10)：1296-1298.
[81]扬尔辅,周强,胡益锋等.基于PCA-RBF神经网络的工业裂解炉收率在线预测软测量方法[J].系统仿真学报.2001,13：194-197.
[82]FISHER R A. The use of multiple measurements in taxonomic problems. Annals of Eugenics,1936,7(2):179-188.
[83]SCHOLKOPF B, SMOLA A J, MULLER K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation,1998,10(5):1299-1319.
[84]YANG J, FRANGI A F, YANG J Y, et al. KPCA plus LDA:a complete kernel fisher discriminant framework for feature extraction and recognition[J]. Pattern Analysis and Machine Intelligence,2005,27(2):230-244.
[85]TENENBAUM J, DE SILVA V, LANGFORD J. A global geometric framework for nonlinear dimensionality reduction. Science,2000,90(5500):2319-2323.
[86]ROWEIS S T, SAUL L K. Nonlnear dimensionality reduction by locally linear embedding. Science,2000,90(5500):2323-2326.
[87]王和勇,郑杰,姚正安.基于聚类和改进距离的LLE方法在数据降维中的应用[J].计算机研究与发展,2006,43(8)：1485-1490.
[88]KANUNGO T, MOUNT D M, NETANYAHU N S, et al. An efficient k-means clustering algorithm:analysis and implementation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
[89]PAL N R, PAL K, KELLER J M, et al. A possibilistic fuzzy c-means clustering algorithm [J]. IEEE Transactions on Fuzzy Systems,2005,13(4):517-530.
[90]高尚,杨静宇.一种新的基于粒子群算法的聚类方法[J].南京航空航天大学学报,2006,38(S1)：62-65.
[91]JAYNES E T. Information theory and statistical mechanics[J]. Physical Review,1957,108: 171-190.
[92]霍映宝,朱长虹,韩之俊.广义最大熵回归分析[J].南京理工大学学报,2006,12(6)：793-796.
[93]RABINER L, JUANG B. An introduction to hidden Markov models[J]. ASSP Magazine, 1986,3(1):4-16.
[94]SVANTE W. Principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems,1987,2(1-3):37-52.
[95]沈欧,李元,吕锋.基于PCA故障诊断的化工过程多智能体优化策略[J].吉林大学学报(工学版),2004,34(z1)：203-206.
[96]汤健,赵立杰,岳恒,等.基于多源数据特征融合的球磨机负荷软测量.浙江大学学报,2010,44(7)：1406-1413.
[97]LEE J M, YOO C K, CHOI S W, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science,2004,59(1):223-234.
[98]KIM S W, OOMMEN B J. On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods[J]. Pattern Recognition,2004,37(2):227-239.
[99]BAUDAT G, ANOUAR F. Feature vector selection and projection using kernels[J]. Neurocomputing,2003,55(1-2):21-38.
[100]SUYKENS J A K, VANDEWALLE J. Least Squares Support Vector Machine Classifiers[J]. Neural Processing Letters,1998,9(3):293-300.
[101]杨奎河,单甘霖,赵玲玲.基于最小二乘支持向量机的汽轮机故障诊断[J].控制与决策,2007,22(7)：778-782.
[102]王剑非,姜斌,冒泽慧.基于LSSVM的卫星姿态控制系统故障诊断[J].控制工程,2008,15(3)：334-336.
[103]安剑奇,吴敏,何勇等.基于料面温度场特征的高炉炉况诊断方法[J].浙江大学学报(工学版),2010,44(7).1276-1281.
[104]余正涛,邹俊杰,赵兴等.基于主动学习的最小二乘支持向量机稀疏化[J].南京理工 大学学报.2012,36(1)：12-17.
[105]SCHOLKOPF B, SMOLA A, MULLER K R. Nolinear component analysis as a kernel eigenvalue problem[J]. Neural Computation,1998,10(5):1299-1319.
[106]DANIAN Z. Reaseach on kernel methods in machine learning[D]. Peking:Tsinghua University,2006.
[107]黄廷祝.块Jacobi迭代阵的收敛性[J].电子科技大学学报,1996,25(6)：663-665.
[108]高海华,杨辉华,王行愚.基于核KPCA特征收取的SVM网络入侵检测方法[J].华东理工大学学报,2006,32(3)：321-326.
[109]赵忠盖,刘飞.基于稀疏核主元分析的在线非线性过程监测[J].化工学报,2008,59(7)：1773-1777.
[110]王新峰,邱静,刘冠军.基于有监督核函数主元分析的故障状态识别[J].测试技术学报,2005,19(2)：200-203.
[111]张亮,黄曙光,郭浩.快速核有监督局部保留投影算法[J].电子与信息学报,2011,33(5)：1049-1054.
[112]PAL N R. A possibilistic fuzzy c-means clustering algorithm[J]. IEEE Transactions on Fuzzy Systems,2005,13(4):508-516.
[113]甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机[J].浙江大学学报(工学版),2007,41(2)：245-248.
[114]WANG J, LU H P, PLATANIOTIS K N, et al. Gaussian kernel optimization for pattern classification[J]. Pattern recognition,2009,42(7):1237-1247.
[115JTAKU K, YUJIN M. Fast Methods for Kernel-based Text Analysis[C]//Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics,2003:24-31.
[116]ZHANG L, ZHOU W D, JIAO J C. Wavelet Support Vector Machine[J]. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS,2004,34(1):34-39.
[117]HSUAN T L, CHIH J L. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods classification[R]. Taipei:National Taiwan University,2003.
[118]张邦琪,朱相.泽.提高云南铜业艾萨炉寿命的实践与探讨[J].有色金属,2005,57(4)：47-53.
[119]姜万录,吴胜强,刘思远.指数加权动态核主元分析法及其在故障诊断中应用[J].机械工程学报,2011,47(3)：63-68.
[120]刘爱伦,袁小艳,俞金寿.基于KPCA-SVC的复杂过程故障诊断[J].仪器仪表学报,2007,28(5)：870-874.
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