| CODE | SCE5109 | ||||||||||||||||||||||||
| TITLE | Estimation and System Identification | ||||||||||||||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||||||||||||||
| ECTS CREDITS | 5 | ||||||||||||||||||||||||
| DEPARTMENT | Systems and Control Engineering | ||||||||||||||||||||||||
| DESCRIPTION | This study-unit introduces the principles of random signals and tools to deal with systems involving such signals. It then presents the principles of system identification where mathematical models are used to represent signals and systems used in signal processing, systems theory and control. The foundations of estimation theory that are needed to obtain an efficient and consistent estimate of the model are presented and put into practice to obtain good working models of real world systems from observed data. Study-Unit Aims: The aims of this study-unit are to: - introduce the principles behind system identification for linear deterministic systems; - present the theory of random variables, operations on random variables, as well as random processes, specifically for the analysis and design of signals and systems; - present different classes of models that can be used to model real world systems. These include parametric and non-parametric models such as autoregressive moving average models and their derivatives, prediction error methods and impulse, step and frequency response models; - introduce estimation theory and analyze different estimation methods that can be used to find unknown model parameters; - present the issues related with model building that help bridge the gap between theory and practice. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - differentiate between parametric and non-parametric models used for system identification; - provide the mathematical representation of different parametric and non-parametric models; - shortlist possible candidate models to represent a real-world signal or system; - identify and apply a suitable estimation method to determine the unknown model parameters from observed data; - apply statistical measures to test the adequacy of a model. 2. Skills: By the end of the study-unit the student will be able to: - apply mathematical models to represent real world signals and systems; - estimate the unknown model parameters by applying a suitable estimation method; - apply adequate statistical measures to identify the suitability of a model in representing a real life system. Main Text/s and any supplementary readings: - Peyton Z. Peebles, 鈥淧robability, Random Variables, and Random Signal Principles鈥�, McGraw-Hill Inc, 2nd Edition, 1987. - Arun K Tangirala, "Principles of System Identification - Theory and Practice", CRC Press, Har/Psc edition, 2014. - Lennart Ljung, "System Identification - Theory for the user", Prentice Hall, 2nd edition, 1999. - George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, "Time Series Analysis", John Wiley & Sons, 4th edition, 2008. |
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| STUDY-UNIT TYPE | Independent Study, Lecture, Practicum, Tutorial, O | ||||||||||||||||||||||||
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| LECTURER/S | Tracey Camilleri |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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