| [1] | 柴天佑.工业过程控制系统研究现状与发展方向.中国科学:信息科学, 2016, 46 (8):1003-1015 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608005.htm Chai Tian-You. Industrial process control systems:research status and development direction. Scientia Sinica Informationis, 2016, 46 (8):1003-1015 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608005.htm |
| [2] | 孙备, 张斌, 阳春华, 桂卫华.有色冶金净化过程建模与优化控制问题探讨.自动化学报, 2017, 43 (6):880-892 http://www.aas.net.cn/CN/abstract/abstract19067.shtml Sun Bei, Zhang Bin, Yang Chun-Hua, Gui Wei-Hua. Discussion on modeling and optimal control of nonferrous metallurgical purification process. Acta Automatica Sinica, 2017, 43 (6):880-892 http://www.aas.net.cn/CN/abstract/abstract19067.shtml |
| [3] | 宋贺达, 周平, 王宏, 柴天佑.高炉炼铁过程多元铁水质量非线性子空间建模及应用.自动化学报, 2016, 42(11):1664-1679 http://www.aas.net.cn/CN/abstract/abstract18956.shtml Song He-Da, Zhou Ping, Wang Hong, Chai Tian-You. Nonlinear subspace modeling of multivariate molten iron quality in blast furnace ironmaking and its application. Acta Automatica Sinica, 2016, 42 (11):1664-1679 http://www.aas.net.cn/CN/abstract/abstract18956.shtml |
| [4] | Zhou P, Chai T Y, Wang H. Intelligent optimal-setting control for grinding circuits of mineral processing. IEEE Transactions on Automation Science and Engineering, 2009, 6 (4):730-743 doi: 10.1109/TASE.2008.2011562 |
| [5] | 柴天佑.复杂工业过程运行优化与反馈控制.自动化学报, 2013, 39(11):1744-1757 http://www.aas.net.cn/CN/abstract/abstract18214.shtml Chai Tian-You. Operational optimization and feedback control for complex industrial processes. Acta Automatica Sinica, 2013, 39 (11):1744-1757 http://www.aas.net.cn/CN/abstract/abstract18214.shtml |
| [6] | 汤健, 田福庆, 贾美英, 李东.基于频谱数据驱动的旋转机械设备负荷软测量.北京:国防工业出版社, 2015. 1-63 Tang Jian, Tian Fu-Qing, Jia Mei-Ying, Li Dong. Load Soft Sensor of Rotating Mechanical Device based on Frequency Spectral Data-Driven. Beijing:National Defense Industrial Press, 2015. 1-63 |
| [7] | Tang J, Chai T Y, Liu Z, Yu W. Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills. Chinese Journal of Chemical Engineering, 2015, 23 (12):2020-2028 doi: 10.1016/j.cjche.2015.10.006 |
| [8] | Tang J, Qiao J F, Wu Z W, Chai T Y, Zhang J, Yu W. Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features. Mechanical Systems and Signal Processing, 2018, 99:142-168 doi: 10.1016/j.ymssp.2017.06.008 |
| [9] | Zeng Y, Forssberg E. Monitoring grinding parameters by vibration signal measurement-a primary application. Minerals Engineering, 1994, 7(4):495-501 doi: 10.1016/0892-6875(94)90162-7 |
| [10] | Tang J, Zhao L J, Zhou J W, Yue H, Chai T Y. Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell. Minerals Engineering, 2010, 23 (9):720-730 doi: 10.1016/j.mineng.2010.05.001 |
| [11] | Lei Y G, He Z J, Zi Y Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2009, 23 (4):1327-1338 doi: 10.1016/j.ymssp.2008.11.005 |
| [12] | Singh G K, AlKazzaz S A S. Isolation and identification of dry bearing faults in induction machine using wavelet transform. Tribology International, 2009, 42 (6):849-861 doi: 10.1016/j.triboint.2008.11.008 |
| [13] | Cusido J, Romeral L, Ortega J A, Rosero J A, Garcia Espinosa A G. Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Transactions on Industrial Electronics, 2008, 55 (2):633-643 doi: 10.1109/TIE.2007.911960 |
| [14] | Riera-Guasp M, Antonino-Daviu J A, Pineda-Sanchez M, Puche-Panadero R, Perez-Cruz J. A general approach for the transient detection of slip-dependent fault components based on the discrete wavelet transform. IEEE Transactions on Industrial Electronics, 2008, 55 (12):4167-4180 doi: 10.1109/TIE.2008.2004378 |
| [15] | Kankar P K, Sharma S C, Harsha S P. Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform. Journal of Vibration and Control, 2011, 17 (14):2081-2094 doi: 10.1177/1077546310395970 |
| [16] | Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Yen N C, Tung C C, Liu H H. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, 1998, 454 (1971):903-995 doi: 10.1098/rspa.1998.0193 |
| [17] | Faiz J, Ghorbanian V, Ebrahimi B M. EMD-Based analysis of industrial induction motors with broken rotor bars for identification of operating point at different supply modes. IEEE Transactions on Industrial Informatics, 2014, 10 (2):957-966 doi: 10.1109/TII.2013.2289941 |
| [18] | Shukla S, Mishra S, Singh B. Power quality event classification under noisy conditions using EMD-Based De-Noising techniques. IEEE Transactions on Industrial Informatics, 2014, 10 (2):1044-1054 doi: 10.1109/TII.2013.2289392 |
| [19] | Li R Y, He D. Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification. IEEE Transactions on Instrumentation and Measurement, 2012, 61 (4):990-1001 doi: 10.1109/TIM.2011.2179819 |
| [20] | Zhao L, Tang J, Zheng W. Ensemble modeling of mill load based on empirical mode decomposition and partial least squares. Journal of Theoretical and Applied Information Technology, 2012, 45 (1):179-191 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=CC0212714595 |
| [21] | 汤健, 柴天佑, 丛秋梅, 苑明哲, 赵立杰, 刘卓, 余文.基于EMD和选择性集成学习算法的磨机负荷参数软测量.自动化学报, 2014, 40 (9):1853-1866 http://www.aas.net.cn/CN/abstract/abstract18454.shtml Tang Jian, Chai Tian-You, Cong Qiu-Mei, Yuan Ming-Zhe, Zhao Li-Jie, Liu Zhuo, Yu Wen. Soft sensor approach for modeling mill load parameters based on EMD and selective ensemble learning algorithm. Acta Automatica Sinica, 2014, 40 (9):1853-1866 http://www.aas.net.cn/CN/abstract/abstract18454.shtml |
| [22] | Wu Z H, Huang N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 2009, 1 (1):1-41 http://d.old.wanfangdata.com.cn/Periodical/dianzixb201305033 |
| [23] | 汤健, 柴天佑, 丛秋梅, 刘卓, 余文.选择性融合多尺度筒体振动频谱的磨机负荷参数建模.控制理论与应用, 2015, 32 (12):1582-1591 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201512002 Tang Jian, Chai Tian-You, Cong Qiu-Mei, Liu Zhuo, Yu Wen. Modeling mill load parameters based on selective fusion of multi-scale shell vibration frequency spectrum. Control Theory Applications, 2015, 32 (12):1582-1591 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201512002 |
| [24] | Tang J, Chai T, Yu W, Zhao L J. Modeling load parameters of ball mill in grinding process based on selective ensemble multisensor information. IEEE Transactions on Automation Science and Engineering, 2013, 10 (3):726-740 doi: 10.1109/TASE.2012.2225142 |
| [25] | 汤健, 柴天佑, 赵立杰, 岳恒, 郑秀萍.融合时频信息的磨矿过程磨机负荷软测量.控制理论与应用, 2012, 29(5):564-570 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK201202054009 Tang Jian, Chai Tian-You, Zhao Li-Jie, Yue Heng, Zheng Xiu-Ping. Soft sensing mill load in grinding process by time/frequency information fusion. Control Theory and Applications, 2012, 29 (5):564-570 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK201202054009 |
| [26] | 汤健, 柴天佑, 刘卓.一种磨机负荷参数软测量方法, 国家发明专利, 201510303525, 2015年6月 Tang Jian, Chai Tian-You, Liu Zhuo. A Soft Measuring Method for Mill Load Parameter, China, Patent 201510303525, June 2015 |
| [27] | Liu H W, Sun J G, Liu L, Zhang H J. Feature selection with dynamic mutual information. Pattern Recognition, 2009, 42 (7):1330-1339 doi: 10.1016/j.patcog.2008.10.028 |
| [28] | Zhou Z H, Wu J X, Tang W. Ensembling neural networks:many could be better than all. Artificial Intelligence, 2002, 137 (1-2):239-263 doi: 10.1016/S0004-3702(02)00190-X |
| [29] | Tang J, Zhang J, Wu Z W, Liu Z, Chai T Y, Yu W. Modeling collinear data using double-layer GA-based selective ensemble kernel partial least squares algorithm. Neurocomputing, 2017, 219:248-262 doi: 10.1016/j.neucom.2016.09.019 |
| [30] | Zhang X M, Kano M, Li Y. Locally weighted kernel partial least squares regression based on sparse nonlinear features for virtual sensing of nonlinear time-varying processes. Computers and Chemical Engineering, 2017, 104:164-171 doi: 10.1016/j.compchemeng.2017.04.014 |
| [31] | Poggio T, Vetter T. Recognition and Structure from One 2D Model View: Observations on Prototypes, Object Classes and Symmetries, Technical Report A. I. Memo 1347, Massachusetts Institute of Technology Cambridge, MA, USA, 1992. |
| [32] | Li L J, Peng Y L, Qiu G Y, Sun Z G, Liu S G. A survey of virtual sample generation technology for face recognition. Artificial Intelligence Review, 2017, 1:1-20 doi: 10.1007/s10462-016-9537-z |
| [33] | Du Y, Wang Y. Generating virtual training samples for sparse representation of face images and face recognition. Journal of Modern Optics, 2016, 63 (6):536-544 doi: 10.1080/09500340.2015.1083131 |
| [34] | Li D C, Wu C S, Tsai T I, Lina Y S. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Computers and Operations Research, 2007, 34 (4):966-982 doi: 10.1016/j.cor.2005.05.019 |
| [35] | Abu-Mostafa Y S. Hints. Neural Computation, 1995, 7 (4):639-671 doi: 10.1162/neco.1995.7.4.639 |
| [36] | An G Z. The effects of adding noise during backpropagation training on a generalization performance. Neural Computation, 1996, 8 (3):643-674 doi: 10.1162/neco.1996.8.3.643 |
| [37] | Li D C, Lin Y S. Using virtual sample generation to build up management knowledge in the early manufacturing stages. European Journal of Operational Research, 2006, 175 (1):413-434 doi: 10.1016/j.ejor.2005.05.005 |
| [38] | Li D C, Fang Y H, Lai Y Y, Hu S C. Utilization of virtual samples to facilitate cancer identification for DNA microarray data in the early stages of an investigation. Information Sciences, 2009, 179 (16):2740-2753 doi: 10.1016/j.ins.2009.04.003 |
| [39] | Chang C J, Li D C, Chen C C, Chen C S. A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities. Computers and Industrial Engineering, 2014, 67 (1):139-145 http://www.sciencedirect.com/science/article/pii/S0360835213003598 |
| [40] | Cho S, Jang M, Chang S. Virtual sample generation using a population of networks. Neural Processing Letters, 1997, 5 (2):21-27 doi: 10.1023/A:1009653706403 |
| [41] | Huang C F, Moraga C. A diffusion-neural-network for learning from small samples. International Journal of Approximate Reasoning, 2004, 35 (2):137-161 doi: 10.1016/j.ijar.2003.06.001 |
| [42] | Li D C, Wen I H. A genetic algorithm-based virtual sample generation technique to improve small data set learning. Neurocomputing, 2014, 143:222-230 doi: 10.1016/j.neucom.2014.06.004 |
| [43] | Chen Z S, Zhu B, He Y L, Yu L A. A PSO based virtual sample generation method for small sample sets:applications to regression datasets. Engineering Applications of Artificial Intelligence, 2017, 59:236-243 doi: 10.1016/j.engappai.2016.12.024 |
| [44] | Gong H F, Chen Z S, Zhu Q X, He Y L. A monte carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem:an empirical study of petrochemical industries. Applied Energy, 2017, 197:405-415 doi: 10.1016/j.apenergy.2017.04.007 |
| [45] | Coqueret G. Approximate norta simulations for virtual sample generation. Expert Systems with Applications, 2017, 73:69-81 doi: 10.1016/j.eswa.2016.12.027 |
| [46] | 汤健, 孙春来, 毛克峰.一种虚拟样本生成方法, 国家发明专利, 201510496474, 2015年8月 Tang Jian, Sun Chun-Lai, Mao Ke-Feng. A Virtual Sample Generation Method China Patent 201510303525, August 2015 |
| [47] | Wang F Y. A big-data perspective on AI:Newton, Merton, and analytics intelligence. IEEE Intelligent Systems, 2012, 27 (5):24-34 http://dl.acm.org/citation.cfm?id=2412706 |
| [48] | 李力, 林懿伦, 曹东璞, 郑南宁, 王飞跃.平行学习-机器学习的一个新型理论框架.自动化学报, 2017, 43(1):1-8 http://www.aas.net.cn/CN/abstract/abstract18984.shtml Li Li, Lin Yi-Lun, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel learning-a new framework for machine learning. Acta Automatica Sinica, 2017, 43 (1):1-8 http://www.aas.net.cn/CN/abstract/abstract18984.shtml |
| [49] | Tang J, Chai T Y, Zhao L J, Yue H. Soft sensor for parameters of mill load based on multi-spectral segments PLS sub-models and on-line adaptive weighted fusion algorithm. Neurocomputing, 2012, 78 (1):38-47 doi: 10.1016/j.neucom.2011.05.028 |
| [50] | Rosipal R, Trejo L J. Kernel partial least squares regression in reproducing kernel Hilbert space. Journal of Machine Learning Research, 2002, 2:97-123 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_0902.4380 |
| [51] | Dhanjal C, Gunn S R, Shawetaylor J. Efficient sparse kernel feature extraction based on partial least squares. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31 (8):1347-1361 doi: 10.1109/TPAMI.2008.171 |
| [52] | Tang J, Yu W, Chai T, Liu Z, Zhou X J. Selective ensemble modeling load parameters of ball mill based on multi-scale frequency spectral features and sphere criterion. Mechanical Systems and Signal Processing, 2016, 66-67:485-504 doi: 10.1016/j.ymssp.2015.04.028 |
| [53] | Joe Qin S. Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control, 2012, 36 (2):220-234 doi: 10.1016/j.arcontrol.2012.09.004 |
| [54] | Yin S, Ding S X, Haghani A, Hao H Y, Zhang P. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control, 2012, 22 (9):1567-1581 doi: 10.1016/j.jprocont.2012.06.009 |
| [55] | Ge Z Q, Song Z H, Gao F R. Review of recent research on data-based process monitoring. Industrial and Engineering Chemistry Research, 2013, 52 (10):3543-3562 doi: 10.1021/ie302069q |
| [56] | Yin S, Li X W, Gao H J, Kaynak O. Data-based techniques focused on modern industry:an overview. IEEE Transactions on Industrial Electronics, 2014, 62 (1):657-667 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6748057 |
| [57] | Cong Y, Wang S, Fan B J, Yang Y S, Yu H B. UDSFS:unsupervised deep sparse feature selection. Neurocomputing, 2016, 196:150-158 doi: 10.1016/j.neucom.2015.10.130 |
| [58] | Liu Z, Chai T Y, Yu W, Tang J. Multi-frequency signal modeling using empirical mode decomposition and PCA with application to mill load estimation, Neurocomputing, 2014, 169:392-402 http://www.sciencedirect.com/science/article/pii/S092523121500421X |
| [59] | Motai, Y. Kernel association for classification and prediction:a survey. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (2):208-223 doi: 10.1109/TNNLS.2014.2333664 |
| [60] | de Lázaro J M B, Moreno A P, Santiago O L, da Silva Neto A J. Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems. Computers and Industrial Engineering, 2015, 87:140-149 doi: 10.1016/j.cie.2015.05.012 |
| [61] | Tang J, Liu Z, Zhang J, Chai T Y, Yu W. Kernel latent features adaptive extraction and selection method for multi-component non-stationary signal of industrial mechanical device. Neurocomputing, 2016, 216:296-309 doi: 10.1016/j.neucom.2016.07.043 |
| [62] | Li D C, Liu C W. Extending attribute information for small data set classfication. IEEE Transactions on Knowledge and Data Engineering, 2010, 24 (3):452-464 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5677515 |
| [63] | Shawe-Taylor J, Anthony M, Biggs N L. Bounding sample size with the Vapnik-Chervonenkis dimension. Discrete Applied Mathematics, 1993, 42 (1):65-73 doi: 10.1016/0166-218X(93)90179-R |
| [64] | Muto Y, Hamamoto Y. Improvement of the Parzen classifier in small training sample size. Intelligent Data Analysis, 2001, 5 (6):477-490 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ024560770 |
| [65] | Raudys S J, Jain A K. Small sample size effects in statistical pattern recognition:Recommendations for Practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13 (3):252-264 doi: 10.1109/34.75512 |
| [66] | Duin R P W. Small sample size generalization. In: Proceedings of the 9th Scandinavian Conference on Image Analysis. Uppsala, Sweden: Mendeley, 1995. 957-964 https://www.mendeley.com/research-papers/small-sample-size-generalization/ |
| [67] | Yang J, Yu X, Xie Z Q, Zhang J P. A novel virtual sample generation method based on Gaussian distribution. Knowledge-Based Systems, 2011, 24 (6):740-748 doi: 10.1016/j.knosys.2010.12.010 |
| [68] | Li D C, Chen L S, Lin Y S. Using Functional Virtual Population as assistance to learn scheduling knowledge in dynamic manufacturing environments. International Journal of Production Research, 2003, 41 (17):4011-4024 doi: 10.1080/0020754031000149211 |
| [69] | Li D C, Wu C S, Tsai T I, Chang F M. Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge. Computers and Operations Research, 2006, 33 (6):1857-1869 doi: 10.1016/j.cor.2004.11.022 |
| [70] | Lin Y S, Li D C. The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems. European Journal of Operational Research, 2010, 207 (1):121-130 doi: 10.1016/j.ejor.2010.03.026 |
| [71] | Lei Y G, Lin J, He Z J, Zuo M J. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2013, 35 (1-2):108-126 doi: 10.1016/j.ymssp.2012.09.015 |
| [72] | Efron B, Tibshirani R. Improvements on Cross-Validation:the 632+ bootstrap method. Journal of the American Statistical Association, 1997, 92 (438):548-560 http://biostatistics.oxfordjournals.org/external-ref?access_num=10.2307/2965703&link_type=DOI |
| [73] | Krzanowski W J, Hand D J. Assessing error rate estimators:the leave-one-out method reconsiderd. Australian and New Zealand Journal of Statistics, 2010, 39 (1):35-46 http://www.mendeley.com/research/assessing-error-rate-estimators-leave-one-method-reconsidered/ |
| [74] | Mevik B H, Cederkvist H R. Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR). Journal of Chemometrics, 2004, 18 (9):422-429 doi: 10.1002/(ISSN)1099-128X |