000190671 001__ 190671
000190671 005__ 20190316235747.0
000190671 037__ $$aLECTURE
000190671 245__ $$aProcess Design and Optimization (MLS-S03): Model Identification by Gradient Methods
000190671 269__ $$a2013
000190671 260__ $$c2013
000190671 336__ $$aTeaching Resources
000190671 500__ $$aThis lecture was given at the School of Applied Sciences (HES) in Fribourg (Switzerland) in 4 hours in November 20, 2013.
000190671 520__ $$aThis lecture describes the following topics: <br><br> • Dynamic Models <br>&nbsp;&nbsp;&nbsp; - Conservation of Mass (Concentration Measurements) <br>&nbsp;&nbsp;&nbsp; - Conservation of Energy (Calorimetry) <br>&nbsp;&nbsp;&nbsp; - Beer’s Law (Spectroscopy) <br><br> • Integration of Dynamic Models <br>&nbsp;&nbsp;&nbsp; - Euler’s Method <br>&nbsp;&nbsp;&nbsp; - Runge-Kutta’s Methods (RK) <br><br> • Linear Regression Problems (OLS) <br>&nbsp;&nbsp;&nbsp; - Calibration-free Calorimetry and Spectroscopy <br><br> • Gradient-based Nonlinear Regression Methods (NLR) <br>&nbsp;&nbsp;&nbsp; - Steepest Descent Method <br>&nbsp;&nbsp;&nbsp; - Newton-Raphson and Newton-Gauss Methods (NG) <br>&nbsp;&nbsp;&nbsp; - Newton-Gauss Levenberg Marquardt Method (NGLM) <br><br> • References
000190671 700__ $$0245987$$g139764$$aBilleter, Julien
000190671 8564_ $$uhttps://infoscience.epfl.ch/record/190671/files/Presentation%20HES%20Fribourg%202013.pdf$$zPublisher's version$$s416286$$yPublisher's version
000190671 909C0 $$0252053$$pLA
000190671 909CO $$ooai:infoscience.tind.io:190671$$qGLOBAL_SET$$pSTI
000190671 917Z8 $$x139764
000190671 917Z8 $$x139764
000190671 917Z8 $$x139764
000190671 937__ $$aEPFL-POLY-190671
000190671 973__ $$aOTHER
000190671 980__ $$aLECTURE