Mixture probabilistic PCR model for soft sensing of multimode processes

[1]  Bogdan Gabrys,et al.  Local learning‐based adaptive soft sensor for catalyst activation prediction , 2011 .

[2]  Lingbo Yu,et al.  Probabilistic principal component analysis with expectation maximization (PPCA-EM) facilitates volume classification and estimates the missing data. , 2010, Journal of structural biology.

[3]  Ling Gao,et al.  Combining orthogonal signal correction and wavelet packet transform with radial basis function neural networks for multicomponent determination , 2010 .

[4]  Ying-wei Zhang,et al.  Complex process monitoring using modified partial least squares method of independent component regression , 2009 .

[5]  R. Wightman,et al.  Multivariate concentration determination using principal component regression with residual analysis. , 2009, Trends in analytical chemistry : TRAC.

[6]  Manabu Kano,et al.  Soft‐sensor development using correlation‐based just‐in‐time modeling , 2009 .

[7]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[8]  Luiz Augusto da Cruz Meleiro,et al.  ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process , 2009, Comput. Chem. Eng..

[9]  ChangKyoo Yoo,et al.  Nonlinear Monitoring and Prediction Model in an Industrial Environmental Process , 2008 .

[10]  Manabu Kano,et al.  Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry , 2008, Comput. Chem. Eng..

[11]  Johan Trygg,et al.  K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space , 2008, BMC Bioinformatics.

[12]  António S. Barros,et al.  Segmented Principal Component Transform–Partial Least Squares regression , 2007 .

[13]  G. Civelekoglu,et al.  Prediction of Bromate Formation Using Multi-Linear Regression and Artificial Neural Networks , 2007 .

[14]  Hans-Peter Kriegel,et al.  Supervised probabilistic principal component analysis , 2006, KDD '06.

[15]  Furong Gao,et al.  Injection molding product weight: Online prediction and control based on a nonlinear principal component regression model , 2006 .

[16]  Dae Sung Lee,et al.  Application of a Moving-Window-Adaptive Neural Network to the Modeling of a Full-Scale Anaerobic Filter Process , 2005 .

[17]  In-Beum Lee,et al.  Fault Detection Based on a Maximum-Likelihood Principal Component Analysis (PCA) Mixture , 2005 .

[18]  Jie Zhang,et al.  A recursive nonlinear PLS algorithm for adaptive nonlinear process modeling , 2005 .

[19]  Furong Gao,et al.  Multirate dynamic inferential modeling for multivariable processes , 2004 .

[20]  Denis Dochain,et al.  State and parameter estimation in chemical and biochemical processes: a tutorial , 2003 .

[21]  In-Beum Lee,et al.  Process monitoring based on probabilistic PCA , 2003 .

[22]  Chi-Tsung Huang,et al.  Estimate of process compositions and plantwide control from multiple secondary measurements using artificial neural networks , 2003, Comput. Chem. Eng..

[23]  R. Christensen,et al.  An equivalence relation between parallel calibration and principal component regression , 2002 .

[24]  Ian Witten,et al.  Data Mining , 2000 .

[25]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[26]  G. Irwin,et al.  Dynamic inferential estimation using principal components regression (PCR) , 1998 .

[27]  J. Kalivas,et al.  Local prediction models by principal component regression , 1997 .

[28]  J. Macgregor,et al.  Development of inferential process models using PLS , 1994 .

[29]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[30]  Gary Montague,et al.  Soft-sensors for process estimation and inferential control , 1991 .