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Fuzzy Petri Nets of Education

Fuzzy Petri Nets of Education. J aroslav Knybel – Univesity of Ostrava. Necessity of Simulation. creation of new study programs optional and selection courses orientation of students Student input informa tion recommended way of passing the studies. Fuzzy Petri Nets.

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Fuzzy Petri Nets of Education

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  1. Fuzzy Petri Nets of Education Jaroslav Knybel – Univesity of Ostrava

  2. Necessity of Simulation • creationof new study programs • optional and selection courses • orientation of students • Student • input information • recommended way of passing the studies University of Ostrava

  3. Fuzzy Petri Nets • Graphicvisualization of simulation • Petri Nets • Open-endedinput information - „some“, „lot“, „small“, „middle“ Use Fuzzy University of Ostrava

  4. Clasic Petri Nets • Place • Transition • Edge • Token University of Ostrava

  5. Clasic Petri Nets Example – two processesandonejointsource University of Ostrava

  6. Classical logic • Transition from one status to second one using IF THEN rules • Conjunction in antecedent • Disjunctionin antecedent University of Ostrava

  7. Conjunction in antecedent • Let’s say that statement C is true only in case that statements A and B are true. Then transcript in Petri netsthe will be following µ(t):AB→C University of Ostrava

  8. Disjuction in antecedent • Let’s say C is true when A or B is true. • Problem– this is a different net (token will be in A and B,so only one will get through) University of Ostrava

  9. Petri netswith inhibitors • PN+inhibitive edge • E.g.: The transition will happen if it doesn’t contain token University of Ostrava

  10. Logicin Petr netswith inhibitors • Conjunction in antecedent • Disjuctionin antecedent University of Ostrava

  11. Fuzzy Petri net IF THEN rules • IF d1 THEN d2 - • IF d1 AND d2 THEN d3 - • IF d1 OR d2 THEN d3 - University of Ostrava

  12. Model of transition through studies • Mandatory, optional, selective subjects • Variousorientations of studies • Initial knowledge of student • Required orientation of student • Volition of suitable subjects University of Ostrava

  13. IF THEN rules • IF (p6) programming (at least) THEN (p7) subject „Basics of programming“ • IF (p0) programming (basics) AND (p1) object programming (at least) THENsubject „the Introduction into the object programming (p2)“ • IF Introduction into the object programming (good) OR Introduction into database systems (partly) THEN (p5) language UML • IF (p3) specialization of database (a lot) THEN (p4) subject Introduction into the database systems • IF Introduction into the database systems (well) THEN Relational database University of Ostrava

  14. Graficalillustration University of Ostrava

  15. Simulation • T0 = 0.84 • T1 = 0.89 • T2 = 0.71 • T3 = 0.97 • Let`s choose initial values P0, P1, P3, P6. • P0 = 0.71 • P1 = 0.58 • P3 = 0.92 • P6 = 0.58 • Output • P2 = 0.49 • P4 = 0.82 • P7 = 0.41 • P5 = 0.80 University of Ostrava

  16. Simulators • Any independent software doesn’t exist for simulation of FPN. • CPN simulator – colourfulPetri nets (simulators where it is possible to set up property of statuses and even of transitions) University of Ostrava

  17. Conclusion • Creation of simulator • Incorporation into the current systems • Extension of PN for Fuzzy modeling application University of Ostrava

  18. The end Jaroslav Knybel – jaroslav.knybel@osu.cz

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