ACS323 Intelligent Systems

Module Description (subject to change)

This module will introduce students to the theme of intelligent systems with special applications to modelling, control, and pattern recognition. Although this technological area can be perceived as being broad, the focus will mainly be on Fuzzy Systems and on interesting synergies such as those between Fuzzy Systems and Artificial Neural Networks (ANN), including the Neuro-Fuzzy architecture. This module should appeal to all students from engineering as well as from science backgrounds who wish to learn more about Artificial Intelligence and Machine-Learning related paradigms, and mostly, how may the related architectures be applied effectively to solve real-world problems, i.e. non-linear, noisy, and the ones that are characterised by uncertainties. This unit is also timely indeed, since knowledge transfer from human to machine and from machine to human and knowledge extraction from data (Big Data) are seen particularly, as vital components for a successful economy, healthy well-being, and clean environment. Finally, the module strikes the too-often difficult balance between theoretical foundations and examples of applications via weekly interactive lectures, laboratory experiments, video demonstrations, and problem solving

Credits: 10 (Autumn semester)

Module Leader

Mahdi Mafouf

Professor Mahdi Mahfouf

Amy Johnson Building

If you have any questions about the module please talk to me during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask questions.

Outside of lectures please contact me via email, or drop in to see me.

Learning Outcomes

Learning Outcomes

By the end of the module students will be able to:

  1. Introduce the concept of ‘machine intelligence’ and identify successful, real-world applications of such technologies and potential future applications. [SM6m, EA5m]
  2. Explain the theoretical principles of Artificial Neural Networks and Fuzzy Logic , and relate those principles to neural-fuzzy modelling and fuzzy control. [SM6m]
  3. Discriminate between real-world systems that require machine-learning based approaches and those that are more suited for conventional solutions, with a particular emphasis on design and analysis for ill-defined, non-linear, high-dimensional and uncertain systems. [EA3m, EA4m, EP8m]
  4. Provide pragmatic/effective solutions and systematic analysis to complex problems which cannot be necessarily defined mathematically, demonstrating the critically evaluate such solutions over conventional alternatives in terms of performance, ease and costs of implementation, transparency to the user, and ease of future maintenance. [EA3m, EA4m, D1m]
  5. Work with hardware and software to strike the often difficult balance between the theoretical/practical analysis/design of complex ill-defined systems characterised by uncertainties and critically evaluate machine-learning based solutions. [EA5m, EP3m, D1m, EP8m]

This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.



  • How can fuzzy systems deal with uncertainty?
  • Fuzzy Sets and their properties
  • Comparison with Classical Sets
  • Operations on Fuzzy Sets
  • Membership Functions
  • Universe of Discourse
  • Fuzzification
  • Defuzzification
  • Fuzzy Rule of Inference
  • Fuzzy Rules Formulation
  • Fuzzy Control Design
  • Neural Fuzzy Modelling
  • Self-Organising Fuzzy Logic Control
  • Fuzzy Predictive Modelling
  • Fuzzy Clustering
Teaching Methods

Learning and Teaching Methods

NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.

Lectures and Tutorials: 24 hours
Laboratories: 12 hours
Independent Study: 64 hours

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available via Blackboard (MOLE).



Asynchronous Assignment (100%)

No resit examination is available for this module.



Although this module does not include marked assignments, it does however involves interactive sessions between the Module Leader (MM) and all students as follows:

  • During 3 "Interactive" Laboratory Sessions dealing with specific problems of modelling control and system identification using Fuzzy technology. MM supervises such sessions personally with the help of able and experienced demonstrators where he can gauge the level of understanding of all students and give them opportunities to gain feedback on their progress.
  • Tutorial Sessions are organised whereby more interactions with students are involved which will enable the module leader to gauge even more the level of engagement and understanding of all students. Students can also ask questions and gain feedback this way.
  • A brief summary of how the class performed in the examination, both on the paper overall and on individual questions will be provided shortly after the examination board. Students will also have the opportunity to view the example answers and to see their scripts.
Student Evaluation

Student Evaluation

Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of the module.

You can view the latest Department response to the survey feedback here.

Recommended Reading

Recommended Reading

  • Passino, K.M. and S. Yurkovich. Fuzzy Control. Addison-Wesley Publishing Company, 1998, ISBN 0-201-18074-X, 2001 [Available in Information Commons, 629.89 (P)]