An introduction to identification from data to model
In order to test a system, engineers must produce a test model. Creating a mathematical model of a system from noisy data is a daunting task that faces many engineers in numerous fields including communications and signal processing, mechanical vibrations, electronics. This course presents a general approach to this problem, with both practical examples and theoretical discussions that give the student a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model.
To participate in this course you will need a general knowledge about system identification and control theory. Concepts as Shannon's law, Nyquist frequency, alias-effect, … you also need to have a good knowledge about statistical parameters like average, variance, correlation, …
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