Signal spectra, linear time-invariant sytems, prediction and filtering, linear and non-linear models, identifiability, non-parametric methods, parameter estimation, maximum likelihood estimation, linear regression, least-squares estimation, the prediction error method, the instrumental variable method, subspace identification, kernel methods, support vector machines, convergence and consistency, modeling accuracy, Cramér-Rao lower bound, numerical optimization, recursive estimation, bias and variance errors, experiment design, applications oriented system identification, choice of identification criterion, model validation, model structure selection, system identification in practice.
FEL3201 Data-Driven Modeling, Basic Course 8.0 credits
Information per course offering
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Course syllabus as PDF
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Course syllabus FEL3201 (Spring 2019–)Information for research students about course offerings
181017-190320
Content and learning outcomes
Course contents
Intended learning outcomes
After the course, the student should be able to:
· describe the general principles for system identification.
· identify systems in a satisfactory manner. This includes choice of excitation signals, model structure and estimation algorithm as well as proper use of model validation.
· analyse basic model properties, such as identifiability and accuracy (bias and variance errors).
Literature and preparations
Specific prerequisites
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- EXA1 - Examination, 8.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Other requirements for final grade
· 15 min oral presentation of a selected topic in one of the lectures
· 80% on weekly home-work problems
· project (preferably on a problem related to the student’s own research)
· 50 % on 72 h take home exam
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.