ELEC97085 (EE4-27) Systems Identification and Learning
Lecturer(s): Prof Thomas Parisini
The aim of the course is to introduce methods for constructing stochastic models of dynamic systems from measurements of input and output signals, and basic techniques for prediction of unknown quantities basing on available sensor data.
This course will equip students with techniques for describing dynamic systems by stochastic models, for estimating parameters in these models from input/output data and to design prediction algorithms making use of data provided by sensors, as well as historical data. Students will also gain an understanding of simple techniques for assessing the quality of parameter estimates in these models.
Stochastic dynamical systems and their description in terms of standard discrete time models; these include, for example, linear difference equation models known as ARMAX (Autoregressive Moving Average models with Exogenous input) models. Least-squares parameter estimation. The statistical properties of parameter estimates. Recursive algorithms for parameter identification. Sources of parameter bias. The effects of over-parameterization. Techniques for selecting model order.
Exam Duration: 3:00hrs
Coursework contribution: 0%
Closed or Open Book (end of year exam): Closed
Oral Exam Required (as final assessment): N/A
Prerequisite module(s): None required
Course Homepage: unavailable
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