Systems Modelling From Data

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Department of Applied Mathematics

Synopsis:

The course provides basic knowledge about modelling static and dynamic systems from measurement data. The aim of course is to give students basic knowledge about methods used for data fitting to various functions, fundamentals of linear systems identification with different type of models, e.g. output error models, autoregressive models etc., fundamentals of nonlinear systems identification with different types of models and different types of functions, e.g. neural networks, local model networks, Takagi-Sugeno fuzzy models and Gaussian Processes. The use of these models for systems control design is given. All topics consist of theoretical basics as well are rich of case studies to illustrate different topics.

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No time-table has been prepared for this course

The course is a part of the following study plans: