A presentation on fuzzy logic
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A presentation on fuzzy logic

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A presentation on fuzzy logic A presentation on fuzzy logic Presentation Transcript

  • AA PRESENTATIONPRESENTATION ONON FUZZY LOGICFUZZY LOGIC PREPARED BY:-PREPARED BY:- PATEL SAHIL(06DME025)PATEL SAHIL(06DME025) SAMA RIYAZ(06DME026)SAMA RIYAZ(06DME026)
  • ARTIFICIAL INTELLIGENCE  “AI is the science of making machine do things that would require intelligence if done by man.”  The logic which we use to have fast response in a critical situations is fuzzy logic.
  • Fuzzy Logic: What is it???  Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth - truth values between "completely true" and "completely false"  It is a different way of looking at the world.  It is a superset of Boolean logic!  It deals with “shades of gray!”
  • A Better Method to Deal With the Real World  Not just “True” and “False.”  Takes on a range of values – True – Mostly True – Half True – Kind of True – False  Values range from 0 to 1. – Including decimal values (0.2, 0.7, etc.)
  • Fuzzy Logic Process “Crisp” Input “fuzzy” input Fuzzification Fuzzy logic “Fuzzy” Output De-Fuzzification “Crisp” Output
  • Fuzzification How tall is Kevin?  Very Tall?  Tall?  Average?  Short?  Very Short?
  • How tall is Kevin?  Very Tall (7 feet)?  Tall (6 feet)?  Average (5 feet)?  Short (4 feet)?  Very Short (3 feet)?
  • Some examplesSome examples If you are 5 feet:  Very tall - 0% - Very Tall (7 feet)?  Tall - 0% - Tall (6 feet)?  Average 100% -Average (5 feet)?  Short - 0% - Short (4 feet)?  Very Short - 0% - Very Short (3 feet)?
  • If you areIf you are 5½ feet:5½ feet:  Very tall - 0%Very tall - 0% -Very Tall (7 feet)?-Very Tall (7 feet)?  Tall -Tall - 50%50% --Tall (6 feet)?Tall (6 feet)?  Average -Average - 50%50% --Average (5 feet)?Average (5 feet)?  Short - 0%Short - 0% -Short (4 feet)?-Short (4 feet)?  Very Short - 0%Very Short - 0% -Very Short (3 feet)?-Very Short (3 feet)? Not in boolean logicNot in boolean logic
  • De-Fuzzification  Two Methods:- 1) Winner Take All -Output “Hard Right” = 70% - It is the winner! - Output = 100 (from output mapping) - Looses some of the smoothness of fuzzy logic. 2) Weighted Average - Output “Hard Right” = 70% - Output “Left” = 20% - Output = 73.3
  • Benefits of Fuzzy System Modeling  Ability to Model Highly Complex Business Problems  Ability to Model System Involving Multiple Experts  Reduce Model Complexity  Improve Handling of Uncertain and Possibilities
  • Common Objections to Fuzzy Logic  Much of the opposition to fuzzy logic is based on the misconception  Fuzzy logic invites the belief that the modeling process generates imprecise answers
  • Applications  ABS Brakes  Expert Systems  Control Units  Bullet train between Tokyo and Osaka  Video Cameras  Automatic Transmissions
  • Conclusion  The exact directions and extent of future developments will be dictated by advancing technology and market forces  Fuzzy logic is a tool and can only useful and powerful when combined with Analytical Methodologies and Machine Reasoning Techniques
  • Any Question?????
  • Thank you