Fundamentals of Fuzzy Logic

6 EC

Semester 1, periode 2

5082FUFL6Y

Eigenaar Bachelor Kunstmatige Intelligentie
Coördinator A. Bilgin
Onderdeel van Bachelor Kunstmatige Intelligentie, jaar 3

Studiewijzer 2016/2017

Globale inhoud

Computational Intelligence (CI), as a subset of AI, has been gaining more attention for the last two decades. AI and CI share the same long-term goal: achieving human intelligence in machines. However, different from AI, CI is based on soft computing methods, which enable adaptation to various situations.

Computational Intelligence has three major areas of interest: Neural Networks, Fuzzy Logic and Evolutionary Computation. This course will focus on one area of interest: Fuzzy Logic (FL).

FL is a form of many-valued logic. It extends the truth values to an arbitrary degree of truth, formally a value in the interval [0, 1]. The aim of FL is to mimic human reasoning in an environment of uncertainty and imprecision (such as real world). FL provides an intuitive approach to modelling human intelligence in machines as it uses high-level linguistic inference. In other words, FL enables the computer to understand natural language.

The overall aim of this course is to introduce students to the fundamental topics in FL literature. Amongst these topics are motivation of fuzzy logic, types of uncertainty, types of fuzzy sets, types of fuzzy logic systems, applications of fuzzy logic systems and the ‘Computing With Words’ paradigm.

Studiemateriaal

Literatuur

  • J. Mendel. 2001. 'Uncertain Rule-Based Fuzzy Logic Systems'. Prentice Hall.
  • Siddique, N. and Adeli, H., 2013. 'Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing'. John Wiley & Sons.

Software

  • MATLAB Fuzzy Logic Toolbox

Leerdoelen

By the end of the course, the students should be able to:

  • demonstrate an understanding of the theoretical aspects of fuzzy logic (such as fuzzy set theory).
  • explain and interpret the notion of linguistic variables and its connection to perceptions and human reasoning.
  • analyse real world applications of fuzzy logic, describe the benefits of using fuzzy logic in real-world problems.
  • apply linguistic modelling, design + implement + tune fuzzy rule based systems.

Onderwijsvormen

  • Laptopcollege
  • Presentatie/symposium
  • Hoorcollege
  • Zelfstudie

Verdeling leeractiviteiten

Activiteit

Aantal uur

Hoorcollege

11

Laptopcollege

10

Presentatie

4

Zelfstudie

140

Aanwezigheid

Aanwezigheidseisen opleiding (OER-B):

  • Voor practica en werkgroepbijeenkomsten met opdrachten geldt een aanwezigheidsplicht. De invulling van deze aanwezigheidsplicht kan per vak verschillen en staat aangegeven in de studiewijzer. Wanneer studenten niet voldoen aan deze aanwezigheidsplicht kan het onderdeel niet met een voldoende worden afgerond. .

Toetsing

Onderdeel en weging Details

Eindcijfer

15%

Attendance and pop quizzes

30%

Exam

Moet ≥ 45 zijn, Herkansbaar

55%

Final Project

35%

Demo and presentation

20%

Report

10%

Bonus

Bonus

Opdrachten

For individual assignments, plagiarism rules strictly apply. This means that you must not copy solutions to exercises from fellow students, books, the Internet, or any other source of information. If you do, and if you get caught, this can have serious consequences. Whenever we are unsure whether or not you may have copied some work, you will be asked to explain the solution and answer some questions.

Fraude en plagiaat

Dit vak hanteert de algemene 'Fraude- en plagiaatregeling' van de UvA. Hier wordt nauwkeurig op gecontroleerd. Bij verdenking van fraude of plagiaat wordt de examencommissie van de opleiding ingeschakeld. Zie de Fraude- en plagiaatregeling van de UvA: www.uva.nl/plagiaat

Weekplanning

Week Monday Wednesday
First slot Second slot First slot Second slot
44 Lecture 0 - Introduction to the course
Lecture 1 - Part 1: Computational Intelligence
Lecture 1 - Part 2: Fuzzy Crash course Lecture 2 - Part 1: Fuzzy logic Lecture 2 - Part 2: Fuzzy logic systems
45 Lecture 3 - Part 1: Uncertainty, human reasoning, perceptions
Lecture 3 - Part 2: Linguistic modelling
Lecture 3 - Part 3: Real-world Applications
Lab session: FL application taxonomy 
Lecture 4 - Part 1: Designing fuzzy logic systems Lab session: FL software taxonomy and FLS implementation tutorial
46 Project pitch presentations Practical Session: FLS implementation tutorial Homework solution FLS example
47 No lecture Exam
48 Paper discussions and project meetings Paper discussions and project meetings
49 Paper discussions and project meetings Paper discussions and project meetings
50 Practical Session: Fuzzy c-means and Python Lecture 6 - Types of FL Lecture 7 - Type-2 FL
51 Project Presentations/Demos on Friday (schedule to be made after week 46)

Rooster

Het rooster van dit vak is in te zien op DataNose.

Aanvullende informatie

The course is taught in English.

Contactinformatie

Coördinator

  • A. Bilgin

You may contact myself for any issues and the TAs for your practical questions. Please note that you will also have the chance to ask your questions in person during the practical sessions. Please check your email and Blackboard page of the course regularly. Announcements will be made through Blackboard and copied to you as email.