1 / 22

Using support vector machines for time series prediction and product quality control

Using support vector machines for time series prediction and product quality control. Uwe Thissen Department of Analytical Chemistry. University of Nijmegen. Regression. Reaction vessel. Composition o f copolymer. Raman. Copolymerisation. Time series prediction:. (Auto)regression.

sunee
Télécharger la présentation

Using support vector machines for time series prediction and product quality control

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using support vector machines for time series prediction and product quality control Uwe Thissen Department of Analytical Chemistry University of Nijmegen

  2. Regression Reaction vessel Composition of copolymer Raman Copolymerisation • Time series prediction: (Auto)regression AutoRegressive Moving Average Models (ARMA) Time Applications • Product quality control: Partial least Squares (PLS)

  3. f(x) i x Ordinary Least Squares (OLS) • Solution:

  4. Solution: f(x) + • Constraints: 0 - x Support Vector Regression (SVR)

  5. Minimise: f(x) + 0 • Constraints: -  * x Support Vector Regression (SVR)

  6. Target Constraints Lagrange Optimisation

  7. Resulting Regression • Regression: • Properties: • Sparseness • Dimension of input is irrelevant • Global and unique • Nonlinear extension

  8. f(x) f(x)  + +  0 0 - - x (x) Nonlinear Regression

  9. Linear: • Nonlinear: • General: Regression Formulas

  10. Kernel Types • Linear: • Polynomial: • Radial basis function: • Exponential RBF:

  11. CH3 (BA-MMA)n + H2C CH COCH2CH2CH2CH3 H2C C COCH3 MMA BA O O 1 Fraction MMA in polymer 0 1 Fraction MMA in solution Copolymerisation (TU Eindhoven)

  12. Characterizing Polymerization

  13. High Resolution Raman

  14. Low Resolution Raman

  15. Prediction Results: Raman Spectra

  16. Conclusion: Copolymerisation • Copolymerisation • SVMs outperform PLS • Low resolution Raman spectra can be used • Optimisation • PLS: very fast (1 parameter) • SVM: relatively slow (3 parameters)

  17. Filtration System P0 P1 Filter Pressure (Teijin Twaron) • ΔP as a measure of product quality

  18. Time Series Training Set2 Time Series 1 Time Series 2

  19. Time Series Prediction Results

  20. Conclusion: Filter Pressure • Filter pressure • SVM outperforms ARMA • Prediction 2.5 hours in advance • Optimisation • ARMA: very fast (2 parameters) • SVM: relatively slow (3 parameters)

  21. Usability Of SV Regression • Performance in industrial applications • SVMs are useful candidates • Lower prediction errors • Usage of low resolution data • Optimisation • PLS, ARMA are fast • SVM is relatively slow

  22. Acknowledgements Head of Department:Prof. Dr. L. Buydens Supervisor:Dr. W. Melssen Students: R. van Brakel, B. Üstün Partners:M. Pepers (University of Eindhoven, NL) Dr. T. de Weijer (Teijin Twaron, Arnhem, NL) Funds: Dutch Technology Foundation (STW)

More Related