This unique text blends together state estimation and chemometrics for the application of advanced data-processing techniques. It further applies system theory in order to develop a modular framework to be implemented on computer for the development of simple intelligent analyzers. Short reviews on the history of state estimation and chemometrics are given, together with examples of the applications described, including classical estimation, state estimation, non-linear estimation, the multi-component, calibration and titration systems and the Kalman filter. The contents are very systematic and build the ideas up logically to appeal to specialist post-graduates working in this area, together with professionals in other areas of chemistry and engineering.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Maße
Höhe: 234 mm
Breite: 156 mm
Gewicht
ISBN-13
978-1-904275-33-6 (9781904275336)
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Schweitzer Klassifikation
Dr. Pierre Cornelis Thijssen studied chemistry at the Radboud University Nijmegen in the Netherlands, obtaining his Master's Degree in 1978. He then moved to the University of Amsterdam in the Netherlands, where he graduated in 1986 with a PhD based on his thesis entitled "State Estimation in Chemometrics,? which is the basis of this book. Since then, Dr. Thijssen has worked for various companies as a laboratory manager and chemometrician.
Autor*in
University of Amsterdam, The Netherlands
Dedication
ABOUT OUR AUTHOR
Chapter 1: Introduction
Publisher Summary
1.1 History
1.2 Chemometrics
1.3 System view
Chapter 2: Classical estimation
Publisher Summary
2.1 Linear model
2.2 Least squares
2.3 Curve fitting
2.4 Recursive approach
2.5 Examples
Chapter 3: State estimation
Publisher Summary
3.1 State space model
3.2 Intermezzo
3.3 Prediction
3.4 Filtering
3.5 Kalman filter
3.6 Smoothing
3.7 Examples
Chapter 4: Statistics
Publisher Summary
4.1 Verification
4.2 Evaluation
4.3 Selection
4.4 Normality
4.5 Example
Chapter 5: Nonlinear estimation
Publisher Summary
5.1 Nonlinear state space model
5.2 Extended Kalman filter
5.3 Iterated extended Kalman filter
5.4 Iterated linearized filter-smoother
5.5 Nonlinear smoothing
5.6 Examples
Chapter 6: The multicomponent system
Publisher Summary
6.1 Multicomponent analysis
6.2 Stochastic drift
6.3 Examples
Chapter 7: The calibration system
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7.1 Linear calibration
7.2 Nonlinear calibration
7.3 Examples
Chapter 8: The titration system
Publisher Summary
8.1 Discrete titration
8.2 Continuous titration
8.3 Nonlinear model
8.4 Examples
Chapter 9: Miscellaneous
Publisher Summary
9.1 Multiple modeling
9.2 Principal components
9.3 Examples
Appendix
Bibliography
Index
