Master of Engineering (MEng)

MEng Electrical and Electronic Engineering with Industrial Experience

*This course is now closed for applications for 2025 entry.

  • Duration: 5 years
  • Year of entry: 2025
  • UCAS course code: H601 / Institution code: M20
  • Key features:
  • Industrial experience
  • Scholarships available
  • Accredited course

Full entry requirementsHow to apply

Course unit details:
System Identification and Artificial Intelligence

Course unit fact file
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
To select EEEN40231 you need to have taken unit EEEN30231 Control Systems II in your 3rd year.

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

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