A novel nonlinear adaptive Mooney-viscosity model based on DRPLS-GP algorithm for rubber mixing process

Chemometrics and Intelligent Laboratory Systems

Volumn 112, Issue , 2012, Pages 17-23

  Zhang, Ze ^a   Song, Kai ^a   Tong, Tuo Peng ^a   Wu, Fang ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

DRPLS GP; Mooney viscosity; Rheological parameters; Rubber mixing process

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; CHEMICAL PROCEDURES; CHINA; CONTROLLED STUDY; DATA ANALYSIS; DISCOUNTED MEASUREMENT RECURSIVE PARTIAL LEAST SQUARES GAUSSIAN PROCESS ALGORITHM; FLOW KINETICS; INTERMETHOD COMPARISON; MOONEY VISCOSITY; MULTIVARIATE ANALYSIS; NOISE; NONLINEAR REGRESSION ANALYSIS; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; ONLINE ANALYSIS; ORTHOGONAL LATENT VARIABLE; PREDICTION; PRIORITY JOURNAL; RUBBER INDUSTRY; RUBBER MIXING; SAMPLING; STATISTICAL PARAMETERS; VISCOSITY;

EID: 84862777809     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2011.12.001     Document Type: Article

References (29)

1. Padmavathi G., Mandan M.G., Mitrab S.P., Chaudhuria K.K. Neural modelling of Mooney viscosity of polybutadiene rubber. Computers and Chemical Engineering 2005, 29:1677-1685.
2. Geladi P., Kowalski B.R. Partial least squares regression: a tutorial. Analytica Chimica Acta 1986, 185:1-17.
3. Wold S., Sjötröm M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58:109-130.
4. Jong S.D. SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 1993, 18:251-263.
5. Helland K., Berntsen H.E., Borgen O.S., Martens H. Recursive algorithm for partial least squares regression. Chemometrics and Intelligent Laborary Systems 1992, 14:129-137.
6. Fang C.Z., Xiao D.Y. Process identification 2001, Tsinghua University Press, Bei Jing.
7. Qin S.J. Recursive PLS algorithms for adaptive data modeling. Computers & Chemical Engineering 1998, 22:503-514.
8. Qin S.J., McAvoy T.J. Non-linear PLS modeling using neural networks. Computers and Chemical Engineering 1992, 16:379-391.
9. Jia R.D., Mao Z.Z., Chang Y.Q., Zhang S.N. Kernel partial robust M-regression as a flexible robust nonlinear modeling technique. Chemometrics and Intelligent Laboratory Systems 2010, 100:91-98.
10. Park J.I., Liu L., Ye X.P., Jeong M.K., Jeong Y.S. Improved prediction of biomass composition for switchgrass using reproducing kernel methods with wavelet compressed FT-NIR spectra, Expert Systems with Applications 2011, In Press.
11. Baffi G., Martin E.B., Morris A.J. Non-linear projection to latent structures revisited. Computers and Chemical Engineering 1999, 23:1293-1307.
12. Adebiyi O.A., Corripio A.B. Dynamic neural networks partial least squares (DNNPLS) identification of multivariable processes. Computers and Chemical Engineering 2003, 27:143-155.
13. Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machine Learning 2006, the Cambridge Press, London.
14. Sivia D., Skilling J. Data Analysis: A Bayesian Tutorial 2006, Oxford Science Publications, London. Second ed.
15. Chen T., Morris J., Martin E. Gaussian process regression for multivariate spectroscopic calibration. Chemometrics and Intelligent Laboratory Systems 2007, 87:59-71.
16. Word S. Personal memories of the early PLS development. Chemometrics and Intelligent Laboratory Systems 2001, 58:83-94.
17. Kesavan P., Lee J.H., Saucedo V., Krishnagopalan G.A. Partial least squares (PLS) based monitoring and control of batch digesters. Journal of Process Control 2000, 10:229-236.
18. Wold S., Ruhe A., Wold H., Dunn W.J. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses, SIAM J. Sci. Statistics and Computing 1984, 5(3):735-743.
19. Haaland D.M., Thomas E.V. Partial least squares methods for spectral analysis: 1. Relation to other quantitative calibration methods and the extraction of qualitative information. 2. Application to simulated and glass spectral data. Analytical Chemistry 1988, 60:1193-1208.
20. Kourti T., Nomikos P., MacGregor J.F. Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. Journal of Process Control 1995, 5:277-284.
21. Jones D.R. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization 2001, 21:345-383.
22. Sacks J., Welch W.J., Mitchell T.J., Wynn H.P. Analysis and design of computer experiments. Statistical Science 1989, 4:409-435.
23. Higdon D., Gattiker J., Williams B., Rightley M. Computer Model Calibration using High-Dimensional Output. Journal of the American Statistical Association 2008, 103:570-583.
24. MacKay D.J.C. Apractical bayesian framework for backpropagation networks. Neural Computation 1992, 4(3):448-472.
25. MacKay D.J.C. Bayesian methods for back propagation networks. Models of Neural Networks II 1993, Springer. V.J.L. Hemmen, E. Domany, K. Schulten (Eds.).
26. Neal R. Regression and Classification Using Gaussian Process Priors. Bayesian Statistics 1997, 6:475-501.
27. Bichon B.J., McFarland J.M., Mahadevan S. Efficient surrogate models for reliability analysis of systems with multiple failure modes, Reliability Engineering & System Safety 2011, In Press.
28. Chen T., Hadinoto K., Yan W.J., Ma Y.F. Efficient meta-modelling of complex process simulations with time space-dependent outputs. Computers and Chemical Engineering 2011, 35:502-509.
29. Devos O., Duponchel L. Parallel genetic algorithm co-optimization of spectral pre-processing and wavelength selection for PLS regression. Chemometrics and Intelligent Laboratory Systems 2011, 107:50-58.