1.
AA
PRESENTATIONPRESENTATION
ONON
FUZZY LOGICFUZZY LOGIC
PREPARED BY:-PREPARED BY:-
PATEL SAHIL(06DME025)PATEL SAHIL(06DME025)
SAMA RIYAZ(06DME026)SAMA RIYAZ(06DME026)
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.
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.
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.)
6.
Fuzzification
How tall is Kevin?
Very Tall?
Tall?
Average?
Short?
Very Short?
7.
How tall is Kevin?
Very Tall (7 feet)?
Tall (6 feet)?
Average (5 feet)?
Short (4 feet)?
Very Short (3 feet)?
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.
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.
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.
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.
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.
Applications
ABS Brakes
Expert Systems
Control Units
Bullet train between Tokyo and Osaka
Video Cameras
Automatic Transmissions
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
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