The document discusses fuzzy logic and its applications. It begins by defining fuzzy logic as a form of logic that accounts for imprecise notions and allows for reasoning with partial truths. It then discusses how fuzzy logic originated from the work of Lotfi Zadeh in 1965 and was first applied commercially in 1980. The document also summarizes key applications of fuzzy logic in control systems, such as temperature controllers and anti-lock braking systems for cars. It concludes that fuzzy logic provides an alternative way to represent real-world attributes linguistically and allows for more efficient and simpler system designs compared to conventional logic-based approaches.

1. Shiva Shrestha
HSM, Hetauda
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2.  Fuzzy sets
 Fuzzy logic
 Judgmental bias
 corrective procedures
 Creativity concepts and approaches
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3. 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

4. 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.

5.  Derived from two words fuzzy and logic.
 a type of logic used to try to make computers
behave like a human brain
 it is a theoretical system used in mathematics,
computing and philosophy which allows theorists
and computers to deal with statements which are
neither true nor false
 Powerful problem solving methodology.
 Incorporates alternative way of thinking
 Multi-valued logic
 Effective and accurate way to describe human
perceptions of decision making.
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6.  In fuzzy logic exact reasoning is viewed as a
limiting case of approximate reasonably
 In fuzzy logic everything is a matter of
degree.
 In fuzzy logic inferences is viewed as
approximation of elastic constraints
 Any logical system can be fuzzified.
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7. 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.

8.  Its an alternative design methodology which
is simpler and faster
 Reduces the design development cycle
 Simplifies design complexity
 Improves time to market
 Improves control performance
 Simplifies implementation
 Reduces hardware costs
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9.  Selecting stocks to purchase
 Retrieving data
 Regulating antilock braking systems in cars
 Auto-focusing in cameras
 Building environmental controls
 Controlling the motion of trains
 Automating the operation of laundry
machines
 Decision making
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10. BENEFITS OF USING FUZZY LOGIC

11. 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 )

12. 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.

13. 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

14.  Complex mathematical term in multi-valued
logic
 Fuzzy set is any condition for which we have
words as: short men, tall women, hot day,
high intelligence etc…. Where the condition
can be between 0 and 1
 Examples of fuzzy sets are motor speed,
boiler pressure, shower- water temperature,
etc.. Too high motor speed, very low boiler
pressure, not hot shower water are subsets of
fuzzy logic
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15.  Fuzzification is an input variable to express
the associated measurement of uncertainity
 Purpose of fuzzification is to interpret
measurements of input variables each
expressed by a real number.
 Descriptive words are used to express
fuzzification like light, very light, medium,
very hot etc..
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16.  Reverse process of fuzzification
 Purpose of defuzzification is to convert each
conclusion obtained by inference engine
which is expressed in terms of fuzzy set to a
real number.
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17. 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.

18. Thankyouvery much!!!
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