Fitting dynamical models to data
Faculteit | Science and Engineering |
Jaar | 2020/21 |
Vakcode | WMIE007-05 |
Vaknaam | Fitting dynamical models to data |
Niveau(s) | master |
Voertaal | Engels |
Periode | semester I b |
ECTS | 5 |
Rooster | rooster.rug.nl |
Uitgebreide vaknaam | Fitting dynamical models to data | ||||||||||||||||||||||||
Leerdoelen | This course aims at providing background and mathematical tools for fitting parameters in a dynamical model to available data. It complements courses on the modeling of (complex) systems. This course is also suitable for anyone who will be involved in the analysis, optimization and control design of dynamical systems, including, electro-mechanical systems, chemical processes, operations, biomedical systems and biological systems. At the end of the course, students will be able: LO1: to construct an ARMAX or IIR or discrete-time state-space model from input-output signals; LO2: to estimate/to identify the unknown parameters in a dynamical model, which fit with the available data, using various computational tools; LO3) to refine/to adapt the parameters recursively based on newly acquired data. |
||||||||||||||||||||||||
Omschrijving | Dynamical modeling has played an important role for the analysis, optimization and control design of systems, including, electro-mechanical systems, chemical processes, operations, biomedical systems and biological systems. The models are generally constructed based on physical laws or phenomenological behavior. These models contain parameters which need to be identified in order to capture the essential systems dynamical behavior. In this course, the students will learn methodologies for estimating the parameters in the models using the available data. The topics that are covered in the class include: - Introduction to difference equations - Auto-correlation and cross-correlation functions - Wiener filter - Least-mean square filter - Recursive Kalman filter and recursive least-square filter - Maximum-likelihood filter - Particle filter and extended Kalman filter - Bayesian filter - State-space realization theory |
||||||||||||||||||||||||
Uren per week | |||||||||||||||||||||||||
Onderwijsvorm |
Hoorcollege (LC), Practisch werk (PRC)
(The practicals are computer practicals.) |
||||||||||||||||||||||||
Toetsvorm |
Opdracht (AST), Schriftelijk tentamen (WE)
(individual asignment, final exam with open questions) |
||||||||||||||||||||||||
Vaksoort | master | ||||||||||||||||||||||||
Coördinator | prof. dr. ir. B. Jayawardhana | ||||||||||||||||||||||||
Docent(en) | prof. dr. ir. B. Jayawardhana | ||||||||||||||||||||||||
Verplichte literatuur |
|
||||||||||||||||||||||||
Entreevoorwaarden | The course unit assumes prior knowledge acquired from Signals and Systems for IEM/BMT, Mechatronics and Numerical Methods. This course prepares student for the IEM IEM Research Master Thesis project. |
||||||||||||||||||||||||
Opmerkingen | There will be a group assignment. Every group has to decide on their own, on the systems that will be identified. Each group has to collect the data themselves. Each group will present their system, the data and the methods in the class room from Week 3 onwards. Take-home test exam: the sheet will be distributed at the last lecture. Final grade = max{take-home test grade, 0.60 x individual assignment + 0.40 take-home test grade}. This course was registered last year with course code TBAFPE-11 |
||||||||||||||||||||||||
Opgenomen in |
|