CSU44001 – Fuzzy Logic and Control Systems

Module CodeCSU44001
Module Name Fuzzy Logic and Control Systems
ECTS Weighting[1]5 ECTS
Semester taughtSemester 1
Module Coordinator/s  Khurshid Ahmad

Module Learning Outcomes

On successful completion of this module, students will be able to:

  • Understanding of uncertainty in data and in modelling
  • Knowledge of fuzzy sets and system Skills in building fuzzy knowledge based systems
  • Competencies in data analytics
  • Application of quasi-intelligent systems to big data analytics

Module Content

This course will introduce you to the exciting new field of fuzzy systems and the related topics in machine learning and the so-called deep learning neural nets.  Fuzzy systems are in almost daily use: your washing machine has fuzzy controls and many of the refrigerators do too.  In both cases, the machines can sense the amount of stuff (clothes or food) and adjust their operations accordingly.  Equally, important is the fact that fuzzy control systems are used in managing financial markets, in operating power plants and transportation systems, and as a basis of systems that will deal help process the and almost every aspect that relies on continuous big data related issue.  In short there is a fuzzy systems for monitoring and tuning. complex systems Fuzzy logic has provided a wonderful new basis for how machines can be made to learn much like humans do especially in domains where uncertainty and approximate reasoning are the order of the day.  Fuzzy logic is the bridge between the more abstract statistical machine learning and the more complex human knowledge acquisition. Fuzzy logic has been developed by computer scientists, psychologists, statisticians, and control engineers over the last 30 odd years. The course will be delivered in five lecture series:

  • 1.             Terminology: Uncertainty, Approximations and Vagueness (2 Lectures)
  • 2.             Fuzzy Sets and Aggregation Operators (5 Lectures)
  • 3.             Fuzzy Logic and Fuzzy Systems (8 Lectures, 3 tutorials)
  • 4.             Fuzzy Control (8 Lectures, 2 tutorials)
  • 5.             Neuro-fuzzy systems including introduction to neural computing (4 Lectures)

Teaching and learning Methods

Lectures, tutorials, online lecture slides, Lectures are traditionally delivered and copies of the lecture notes covering each of the five key topics are circulated in advance of the lectures.  All lectures are available on the TCD Blackboard.

Assessment Details

Assessment ComponentBrief Description Learning Outcomes Addressed% of totalWeek setWeek Due
Take home Examination (limited Time)2 hour written examinationLO1, LO2,
LO3, LO4, LO5, LO6
100%

Reassessment Details

Take home limited time 2 hour written examination (100%)

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by: 33 hours
Lecture28 hours
Consultation with the Lecturer5 hours
Independent study (outside scheduled contact hours), broken down by:36 hours
Preparation for classes and review of material (including preparation for examination, if applicable30 hours
Total Hours66 hours

Recommended Reading List

Lotfi Zadeh and Rafik A. Aliev (2019). Fuzzy Logic Theory and Applications. Denver: World Scientific Press.
Negnevitsky, Michael (2002). Artificial Intelligence: A Guide to Intelligent Systems (1st Edition). Harlow:Pearson Education Ltd. (Chapter 4, pp 87-128). (Available at Hamilton Library Open-access Collection)
Kruse, Rudolf., Gebhardt, J., and Klawonn, F. (1994). Foundations of Fuzzy Systems. New York: John Wiley and Sons. (Chapter 2 for fuzzy sets and Chapter 4 for fuzzy control) (Available through Trinity Library but have to wait for it to be called from Santry Collection)

Module Pre-requisites

Prerequisite modules:

Other/alternative non-module prerequisites:

Module Co-requisites

None

Module Website

Blackboard

[1] TEP Glossary

[2] TEP Guidelines on Workload and Assessment