Walczak B, Massart D L. Dealing with Missing Data: PartⅠ. Chemometrics and Intelligent Laboratory Systems, 2001, 58(1): 15-27
[2]
Walczak B, Massart D L. Dealing with Missing Data: Part Ⅱ. Chemometrics and Intelligent Laboratory Systems, 2001, 58(1): 29-42
[3]
Nelson P R C, Taylar P A, MacGregor J F. Missing Data Methods in PCA and PLS: Score Calculations with Incomplete Observation. Chemometrics and Intelligent Laboratory Systems, 2001, 35(1): 45-65
[4]
Nelson P R C, Taylar P A, MacGregor J F. The Impact of Missing Measurements on PCA and PLS Prediction and Monitoring Application. Chemometrics and Intelligent Laboratory Systems, 2006, 80(1): 1-12
[5]
Goulding P R, Lennox B, Sandoz D J, et al. Fault Detection in Continuous Processes Using Multivariate Statistical Methods. International Journal of Systems Science, 2000, 31(11): 1459-1471
[6]
Lieftucht D, Kruger U, Irwin G W. Improved Diagnosis of Sensor Faults Using Multivariate Statistics // Proc of the American Control Conference. Boston, USA, 2004, Ⅴ: 4403-4407
[7]
Lee J M, Yoo C, Choi S W, et al. Nonlinear Process Monitoring Using Kernel Principal Component Analysis. Chemical Engineering Science, 2004, 59(1): 223-234
[8]
Tan S, Mavrovouniotis M L. Reducing Data Dimensionality through Optimizing Neural Network Inputs. AIChE Journal, 1995, 41(6): 1471-1480
[9]
Kramer M A. Nonlinear Principal Component Analysis Using Autoassociative Neural Networks. AIChE Journal, 1991, 37(2): 233-243
[10]
Saegusa R, Sakana H, Hashimoto S. Nonlinear Principal Analysis to Preserve the Order of Principal Components. Neurocomputing, 2004, 61(1): 57-70
[11]
Zhao Zhonggai, Liu Fei, Xu Baoguo. Nonlinear Principal Component Analysis Based on Hierarchical Input-Training Neural Network. Information and Control, 2005, 34(6): 656-659 (in Chinese) (赵忠盖,刘 飞,徐保国.一种基于分级输入训练神经网络的非线性主元分析.信息与控制, 2005, 34(6): 656-659)
[12]
Dong D, McAvoy T J. Nonlinear Principal Component Analysis Based on Principal Curves and Neural Networks. Computers and Chemical Engineering, 1996, 20(1): 65-78