Kadlec P，Grbic R，Gabrys B.Review of adaptation mechanisms for data-driven soft sensors［J］.Computers and Chemical Engineering，2011，35(1):1-24.
[2]
Du W L，Guan Z Q，Qian F.The time series soft-sensor modeling based on Adaboost LS-SVM［C］//The 8th World Congress on Intelligent Control and Automation(WCICA).Jinan，2010:1491-1495.
[3]
Wang H Q，Li P，Gao F R，et al.Kernel classifier with adaptive structure and fixed memory for process diagnosis ［J］.AIChE Journal，2006，52(10):3515-3531.
[4]
刘毅，金福江，高增梁.时变过程在线辨识的即时递推核学习方法研究［J］.自动化学报，2013，39(5):602-609.(Liu Yi，Jin Fu-jiang，Gao Zeng-liang.Online identification of time-varying processes using just-in-time recursive kernel learning approach［J］.Acta Automatica Sinica，2013，39(5):602-609.)
[5]
葛志强，刘毅，宋执环，等.一种基于局部模型的非线性多工况过程检测方法［J］.自动化学报，2008，34(7):792-797.(Ge Zhi-qiang，Liu Yi，Song Zhi-huan，et al.Local model based monitoring for nonlinear multiple mode process［J］. Acta Automatica Sinica ，2008，34(7):792-797.)
[6]
Cheng C，Chiu M S.A new data-based methodology for nonlinear process modeling［J］.Chemical Engineering Science，2004，59(13):2801-2810.
[7]
Fujiwara K，Kano M，Hasebe S.Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design［J］.Control Engineering Practice，2010，20(4):371-378.
[8]
Wang L W，Wang X，Feng J F.Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition［J］.Pattern Recognition，2006，39(3):456-464.
[9]
Raich A，Cinar A.Statistical process monitoring and disturbance diagnosis in multivariable continuous processes［J］.AIChE Journal，1994，42(4):995-1009.
[10]
罗健旭，邵惠鹤.应用多神经网络建立动态软测量模型［J］.化工学报，2003，54(12):170-177.(Luo Jian-xu ，Shao Hui-he.Developing dynamic soft sensors using multiple neural networks［J］.Journal of Chemical Industry and Engineering，2003，54(12):170-177.)
