- UCAS course code
- H601
- UCAS institution code
- M20
Master of Engineering (MEng)
MEng Electrical and Electronic Engineering with Industrial Experience
*This course is now closed for applications for 2025 entry.
- Typical A-level offer: AAA including specific subjects
- Typical contextual A-level offer: AAB including specific subjects
- Refugee/care-experienced offer: ABB including specific subjects
- Typical International Baccalaureate offer: 36 points overall with 6,6,6 at HL, including specific requirements
Course unit details:
System Identification and Artificial Intelligence
Unit code | EEEN40231 |
---|---|
Credit rating | 15 |
Unit level | Level 4 |
Teaching period(s) | Semester 1 |
Available as a free choice unit? | No |
Overview
The unit has two different parts.
Part A - System Identification
Examplar system identification problems.
Measurements and statistics.
Non-parametric methods: Time and frequency domain
Least square problem. Statistic foundation.
Parametric methods (ARX, OE).
Input design.
Optimisation: gradient method for OE.
Recursive estimation.
Validation.
Part B - Artificial Intelligence (for dynamic systems modelling)
Introduction to AI.
Neural network models (single-layer and multiple-layer neural networks).
Deep learning (neural networks).
Learning and optimisation methods.
Performance evaluation.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Control Systems II | EEEN30231 | Pre-Requisite | Compulsory |
Aims
The unit aims to give students an understanding of how system identification and artificial intelligent algorithms can be used to find models of dynamic systems; how least squares and optimisation approaches can be used for parameter estimation; the influence of noise on the parameter estimation and the relevance of measurement theory for the identification process.
Learning outcomes
On successful completion of the course, a student will be able to:
ILO 1: Test and validate the neural networks models with different selecting criteria.
ILO 2: Optimise the neural networks model parameters with input-output data.
ILO 3: Apply least squares and gradient descent optimization algorithms in the context of System Identification in MATLAB.
ILO 4: Demonstrate understanding statistical concepts applied in measurement theory.
ILO 5: Demonstrate understanding of techniques for identifying dynamic systems.
ILO 6: Apply the neural networks models for constructing dynamic models.
ILO 7: Demonstrate the understanding AI and their essential steps for dynamic models.
Teaching and learning methods
Lectures, tutorial and laboratory.
Assessment methods
Method | Weight |
---|---|
Written exam | 80% |
Report | 20% |
Feedback methods
Examination - feedback will be given after the exam board.
System Identifaction coursework - individual feedback is provided 3 weeks after submission.
AI coursework - individual feedback is provided 3 weeks after submission.
Recommended reading
Söderström, T. (1989). System Identification. Prentice Hall.
Ljung, L. (1987). System Identification: Theory for the User. Prentice-Hall.
Goodwin, G.C., & Payne, R.L. (Ed.). (1977). Dynamic System Identification: Experiment Design and Data Analysis. Academic Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Alpaydin, E. (2020). Introduction to Machine Learning (4th ed.). MIT Press
Study hours
Scheduled activity hours | |
---|---|
Lectures | 42 |
Practical classes & workshops | 6 |
Tutorials | 6 |
Independent study hours | |
---|---|
Independent study | 96 |
Teaching staff
Staff member | Role |
---|---|
Ognjen Marjanovic | Unit coordinator |
Long Zhang | Unit coordinator |