Course Overview

This is an introductory course in system identification, the process of developing or improving a mathematical representation of a physical system using experimental data. This course focuses equally on theoretical and practical aspects of the subject. Students will learn key mathematical skills including linear time-invariant systems, random processes, and basic estimation techniques. Practical system identification skills such as input signal design, system excitation, and model validation will also be discussed. Students are required to integrate the knowledge into their works of final projects.

Learning Achievement

1. Review of linear systems 2. Random variables and random processes 3. Least-square estimation 4. Non-parametric model identification 5. Parametric model identification 6. State-space methods 7. System identification in practice 8. Advanced topics* (subspace identification, time varying or nonlinear systems) 9. Final project presentation

Competence

Course prerequisites

Undergraduate-level Control Systems, and/or Signal and Systems. Basic/working knowledge about linear algebra, linear dynamical systems, state-space models, and Fourier, Laplace, and Z-transforms.

Grading Philosophy

Course schedule

Course type

Online Course Requirement

Instructor

Kuen-Yu Tsai

Other information

(College of Electrical Engineering and Computer Science) Graduate Institute of Electronics Engineering,
(College of Electrical Engineering and Computer Science) Graduate Institute of Electrical Engineering,
Non-degree Program: Transprotation Electrification Technology Program

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