Lecture20

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  • 1. Introduction to Machine Learning Lecture 20 Genetic Fuzzy Systems Albert Orriols i Puig http://www.albertorriols.net htt // lb t i l t aorriols@salle.url.edu Artificial Intelligence – Machine Learning g g Enginyeria i Arquitectura La Salle Universitat Ramon Llull
  • 2. Recap of Lecture 19 Slide 2 Artificial Intelligence Machine Learning
  • 3. Today’s Agenda Continuing with the GFS topics Genetic tuning 1. Genetic rule learning 2. Genetic rule selection 3. Genetic DB learning 4. Simultaneous genetic learning of KB components 5. 5 Genetic learning of KB components and inference engine 6. parameters Applications Slide 3 Artificial Intelligence Machine Learning
  • 4. 2. Genetic Rule Learning How do I get my rules? g y The expert may provide me with a set of rules I may need t learn th d to l them Assume Mamdani-type rules Slide 4 Artificial Intelligence Machine Learning
  • 5. 2. Genetic Rule Learning Several models Pittsburgh-style LCSs Michigan-style LCSs Mi hi t l LCS IRL methods GCCL Slide 5 Artificial Intelligence Machine Learning
  • 6. Membership and Rule Tunnig Slide 6 Artificial Intelligence Machine Learning
  • 7. 3. Genetic Rule Selection Select the best rules A bunch of rules is defined The Th GA selects the best ones with th aim of l t th b t ith the i f Getting the best ones Getting G tti a compact rule base t lb Slide 7 Artificial Intelligence Machine Learning
  • 8. 3. Genetic Rule Selection Example of rule selection p Slide 8 Artificial Intelligence Machine Learning
  • 9. 4. Genetic DB Learning Learning the membership function shapes by a GA g p p y Do not mix with membership function tuning Now we are l N learning th shape i the h Slide 9 Artificial Intelligence Machine Learning
  • 10. 5. Simultaneous Learning of KB Components There is a strong dependency between RB and DB gp y Tune them altogether The Th search space i h increases! ! But, since they are dependant, it may improve the result Slide 10 Artificial Intelligence Machine Learning
  • 11. 5. Simultaneous Learning of KB Components Slide 11 Artificial Intelligence Machine Learning
  • 12. 6. Learning of KB and IE Par Example of learning the rule base and the inference connective parameters Slide 12 Artificial Intelligence Machine Learning
  • 13. 6. Learning of KB and IE Par Slide 13 Artificial Intelligence Machine Learning
  • 14. Applications Some cool applications among many: Control of heating and air conditioning systems 1. Anti-lock break systems 2. Robot control 3. 3 Slide 14 Artificial Intelligence Machine Learning
  • 15. Control of Heating and AC The problem p Change the speed of a heater fan, based off the room temperature a d humidity. e pe a u e and u d y A temperature control system has four settings Cold, C l Warm, and H C ld Cool, W d Hot Humidity can be defined by: Low, Medium, and High Using this we can define the initial rule base Slide 15 Artificial Intelligence Machine Learning
  • 16. Control of Heating and AC Initial DB Slide 16 Artificial Intelligence Machine Learning
  • 17. Control of Heating and AC Objectives to be minimized j Slide 17 Artificial Intelligence Machine Learning
  • 18. Control of Heating and AC Tuned data base Slide 18 Artificial Intelligence Machine Learning
  • 19. ABS Nonlinear and dynamic in nature y Inputs for Intel Fuzzy ABS are derived from Brake Bk 4 WD Feedback Wheel speed Ignition Outputs Pulsewidth Error lamp Slide 19 Artificial Intelligence Machine Learning
  • 20. Robot Control Sensorial inputs p Distance to objects Angles … Outputs O Speed of wheels Rotation Pioneer II AT robot … Following a mobile object Following walls Slide 20 Artificial Intelligence Machine Learning
  • 21. Next Class Reinforcement Learning and LCSs Slide 21 Artificial Intelligence Machine Learning
  • 22. Introduction to Machine Learning Lecture 20 Genetic Fuzzy Systems Albert Orriols i Puig http://www.albertorriols.net htt // lb t i l t aorriols@salle.url.edu Artificial Intelligence – Machine Learning g g Enginyeria i Arquitectura La Salle Universitat Ramon Llull