CSU44001 – Fuzzy Logic and Control Systems

Module Code	CSU44001
Module Name	Fuzzy Logic and Control Systems
ECTS Weighting [1]	5 ECTS
Semester Taught	Semester 1
Module Coordinator/s	Khurshid Ahmad

Power point Presentation for CSU44001

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:

Terminology: Uncertainty, Approximations and Vagueness (2 lectures);
Fuzzy Sets and Aggregation Operators (5 lectures);
Fuzzy Logic and Fuzzy Systems (8 lectures, 3 tutorials);
Fuzzy Control (8 lectures, 2 tutorials);
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. Due to COVID-19, the examination is by an open-book (coursework equivalent) which subsumes the coursework element.

Assessment Details

Assessment Component	Brief Description	Learning Outcomes Addressed	% of Total	Week Set	Week Due
Coursework	Written Submission (24 hours)	LO1, LO2, LO3, LO4, LO5,	100%	Week 9	Week 10

Reassessment Details

Coursework (100%).

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:	33 hours
Lecture	28 hours
Consultation with the Lecturer	5 hours
Independent Study (outside scheduled contact hours), broken down by:	36 hours
Preparation for classes and review of material (including preparation for examination, if applicable)	25 hours
Completion of assessments (including examination, if applicable)	11 hours
Total Hours	69 hours

Module Pre-requisites

Prerequisite modules: N/A

Other/alternative non-module prerequisites: N/A

Module Co-requisites

N/A

Module Website

Blackboard

[1] TEP Glossary

[2] TEP Guidelines on Workload and Assessment