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Course presentation: FLA Fuzzy Logic and Applications

Course presentation: FLA Fuzzy Logic and Applications. 4 CTI, 2 nd semester Doru Todinca http://staff.cs.upt.ro/~todinca/ in Courses presentation http://staff.cs.upt.ro/~todinca/courses_pres.html doru.todinca@cs.upt.ro. Course web page: http://staff.cs.upt.ro/~todinca/FLA/FLA.html

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Course presentation: FLA Fuzzy Logic and Applications

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  1. Course presentation:FLAFuzzy Logic and Applications 4 CTI, 2nd semester Doru Todinca http://staff.cs.upt.ro/~todinca/ in Courses presentation http://staff.cs.upt.ro/~todinca/courses_pres.html doru.todinca@cs.upt.ro Course web page: http://staff.cs.upt.ro/~todinca/FLA/FLA.html The web page is under construction ! The course in Romanian: http://staff.cs.upt.ro/~todinca/fl/curs_fl.html Username: fl, passwd: fl_2007

  2. Course description • Structure: 2 hours lectures, 1 hour lab/week, all doubled and compressed in 7 weeks • Final grade: 50% lab, 50% written exam • Goal: to acquire knowledge about fuzzy sets theory that can be applied in computer engineering, automation or telecommunications • How to reach the goal: • Theoretical background is given at the lectures • Presenting applications of fuzzy logic in the above-mentioned domains (lectures, labs) • Applying fuzzy inference to different problems (the FLC code is made available)

  3. Fuzzy Logic • It is a mathematical theory that can work with uncertain and/or subjective information • Fuzzy set theory extends the classic sets in the sense that, for a fuzzy set, an element belongs in a certain degree (between 0 and 1) to that set • Different domains (mathematical or non mathematical) have been extended through the framework of fuzzy logic: • Fuzzy sets, fuzzy logic • Fuzzy relations, approximate reasoning (fuzzy inference) • Fuzzy arithmetic, fuzzy automata, fuzzy flip-flops, etc • Fuzzy inference is the most applied in engineering: • It is based on linguistic variables (like age, distance, speed, etc), that have linguistic terms (young, middle age, old; small, medium, or big speed, etc) • And on fuzzy IF-THEN rules in the form: IF premises THEN conclusion • One fact (a set of measured values of the inputs) activates one or more rules in different degrees • The active rules combine, according to a set of mathematical relations (formulae)

  4. …and Applications • Fuzzy logic is successfully applied in the following situations: • If we work with imprecise information • If we cannot establish a mathematical model, or if the model is too complex and we cannot solve it • In computer engineering: • Fuzzy inference circuits (FLC- Fuzzy Logic Controllers) , expert systems, load balancing problems, etc. • Fuzzy automata, fuzzy flip-flops • In control engineering: fuzzy control (using FLCs) • Less studied at this course • In telecommunications: • The increase of data (versus voce) traffic allow the AI techniques (fuzzy logic, neural networks, genetic algorithms, etc) to gradually replace the “old” stochastic methods, like queueing theory, Markov models, etc

  5. The lab • Lab assignments. Alternatives: • Modeling and simulation of fuzzy circuits (fuzzy automata, fuzzy flip-flops) • Applications of FL in telecomm (mostly mobile communications) • Followed by a10-15 minutes presentation of the results • Or, a 10-15 minutes presentation of a scientific paper from the web page of the course. Goals: • Reading one or more scientific papers • “Training” for the final year project presentation • First two topics can be also final year projects.

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