FuzzyLogic.ppt

FUZZY LOGIC
05 Atharva Tanawade
07 Atharva Mahabal

OVERVIEW
 What is Fuzzy Logic?
 Where did it begin?
 Fuzzy Logic vs. Neural Networks
 Fuzzy Logic in Control Systems
 Fuzzy Logic in Other Fields
 Future

WHAT IS FUZZY LOGIC?
 Definition of fuzzy
 Fuzzy – “not clear, distinct, or precise; blurred”
 Definition of fuzzy logic
 A form of knowledge representation suitable for
notions that cannot be defined precisely, but which
depend upon their contexts.

TRADITIONAL REPRESENTATION OF
LOGIC
Slow Fast
Speed = 0 Speed = 1
bool speed;
get the speed
if ( speed == 0) {
// speed is slow
}
else {
// speed is fast
}

FUZZY LOGIC REPRESENTATION
CONT.
Slowest Fastest
float speed;
get the speed
if ((speed >= 0.0)&&(speed < 0.25)) {
// speed is slowest
}
else if ((speed >= 0.25)&&(speed < 0.5))
{
// speed is slow
}
else if ((speed >= 0.5)&&(speed < 0.75))
{
// speed is fast
}
else // speed >= 0.75 && speed < 1.0
{
// speed is fastest
}
Slow Fast

FUZZY LOGIC REPRESENTATION
 For every problem
must represent in
terms of fuzzy sets.
 What are fuzzy
sets?
Slowest
Fastest
Slow
Fast
[ 0.0 – 0.25 ]
[ 0.25 – 0.50 ]
[ 0.50 – 0.75 ]
[ 0.75 – 1.00 ]

ORIGINS OF FUZZY LOGIC
 Traces back to Ancient Greece
 Lotfi Asker Zadeh ( 1965 )
 First to publish ideas of fuzzy logic.
 Professor Toshire Terano ( 1972 )
 Organized the world's first working group on fuzzy
systems.
 F.L. Smidth & Co. ( 1980 )
 First to market fuzzy expert systems.

FUZZY LOGIC VS. NEURAL
NETWORKS
 How does a Neural Network work?
 Both model the human brain.
 Fuzzy Logic
 Neural Networks
 Both used to create behavioral
systems.

FUZZY LOGIC IN CONTROL
SYSTEMS
 Fuzzy Logic provides a more efficient and
resourceful way to solve Control Systems.
 Some Examples
 Temperature Controller
 Anti – Lock Break System ( ABS )

TEMPERATURE CONTROLLER
 The problem
 Change the speed of a heater fan, based off the room
temperature and humidity.
 A temperature control system has four settings
 Cold, Cool, Warm, and Hot
 Humidity can be defined by:
 Low, Medium, and High
 Using this we can define
the fuzzy set.

ANTI LOCK BREAK SYSTEM ( ABS )
 Nonlinear and dynamic in nature
 Inputs for Intel Fuzzy ABS are derived from
 Brake
 4 WD
 Feedback
 Wheel speed
 Ignition
 Outputs
 Pulsewidth
 Error lamp

FUZZY LOGIC IN OTHER FIELDS
 Business
 Hybrid Modeling
 Expert Systems

CONCLUSION
 Fuzzy logic provides an alternative way to
represent linguistic and subjective attributes of
the real world in computing.
 It is able to be applied to control systems and
other applications in order to improve the
efficiency and simplicity of the design process.

FuzzyLogic.ppt

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