0
AA
PRESENTATIONPRESENTATION
ONON
FUZZY LOGICFUZZY LOGIC
PREPARED BY:-PREPARED BY:-
PATEL SAHIL(06DME025)PATEL SAHIL(06DME0...
ARTIFICIAL INTELLIGENCE
 “AI is the science of making machine do things that
would require intelligence if done by man.”
...
Fuzzy Logic: What is it???
 Fuzzy logic is a superset of conventional (Boolean)
logic that has been extended to handle th...
A Better Method to Deal With
the Real World
 Not just “True” and “False.”
 Takes on a range of values
– True
– Mostly Tr...
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)?
 Averag...
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...
De-Fuzzification
 Two Methods:-
1) Winner Take All
-Output “Hard Right” = 70%
- It is the winner!
- Output = 100 (from ou...
Benefits of Fuzzy System Modeling
 Ability to Model Highly Complex Business Problems
 Ability to Model System Involving ...
Common Objections to Fuzzy Logic
 Much of the opposition to fuzzy logic is based on the
misconception
 Fuzzy logic invit...
Applications
 ABS Brakes
 Expert Systems
 Control Units
 Bullet train between Tokyo and Osaka
 Video Cameras
 Automa...
Conclusion
 The exact directions and extent of future
developments will be dictated by advancing
technology and market fo...
Any Question?????
Thank you
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A presentation on fuzzy logic

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  1. 1. AA PRESENTATIONPRESENTATION ONON FUZZY LOGICFUZZY LOGIC PREPARED BY:-PREPARED BY:- PATEL SAHIL(06DME025)PATEL SAHIL(06DME025) SAMA RIYAZ(06DME026)SAMA RIYAZ(06DME026)
  2. 2. 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.
  3. 3. 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!”
  4. 4. 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.)
  5. 5. Fuzzy Logic Process “Crisp” Input “fuzzy” input Fuzzification Fuzzy logic “Fuzzy” Output De-Fuzzification “Crisp” Output
  6. 6. Fuzzification How tall is Kevin?  Very Tall?  Tall?  Average?  Short?  Very Short?
  7. 7. How tall is Kevin?  Very Tall (7 feet)?  Tall (6 feet)?  Average (5 feet)?  Short (4 feet)?  Very Short (3 feet)?
  8. 8. 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)?
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. Applications  ABS Brakes  Expert Systems  Control Units  Bullet train between Tokyo and Osaka  Video Cameras  Automatic Transmissions
  14. 14. 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
  15. 15. Any Question?????
  16. 16. Thank you
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