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ARTIFICIAL NEURAL NETWORKS
FUZZY LOGIC
(AUTOMATED AUTOMOBILES)
Introduction
 Fuzzy Logic control system is used to
  control the speed of the car based on the
  obstacle sensed.
 Fuzzy logic is best suited for control
  applications, such as temperature control,
  traffic control or process control.
Fuzzy Vs. Probability
 Fuzziness describes the ambiguity of
  an event.
 whereas probability describes the
  uncertainty in the occurrence of the
  event.
Complexity of a System vs. Precision in
the model of the System:
   For systems with little
complexity, closed-form
mathematical expressions
provide precise descriptions
of the systems.
 For systems that are a
little more complex, artificial
neural networks, provide
powerful and robust.
 For systems with more complex,
Fuzzy system is used.
Fuzzy Set vs. Crisp Set:
 A classical set is defined by crisp
  boundaries; i.e., there is no uncertainty
  in the prescription or location of the
  boundaries of the set.
 A fuzzy set, on the other hand, is
  prescribed by vague or ambiguous
  properties.
Membership function and features of
membership function:
  Membership function characterize the
   fuzziness in a fuzzy set.
 The core comprises those elements X of the
   universe such that A(x) = 1.
 The support comprises
   those elements X of the
   universe such that
     A(x) > 0.
 The boundaries comprise
  these elements X of the universe
  such that 0< A(x) <1.
Fuzzification and Defuzzification

   Fuzzification is the process of making a crisp
    quantity fuzzy.
   Defuzzification is the conversion of a fuzzy
    quantity to a precise quantity.
Fuzzy Logic Control System
 Obstacle   Sensor Unit:
       The car consists of a
sensor in the front panel to
sense the presence of the
obstacle.
Sensing Distance
 The sensing distance depends upon the
  speed of the car and the speed can be
  controlled by gradual anti skid braking
  system.
 The speed of the car is taken as the input
  and the distance sensed by the sensor is
  controlled.
Input Membership Function:




Output Membership Function:
The defuzzified
values are obtained
and the variation of
speed with sensing
distance is plotted
as a surface graph
Speed Control
 Speed breaker
 Fly Over
The angle is taken as the input and output speed is
controlled.
Input Membership Function:




Output Membership Function:
From the graph it
is clear that the
speed becomes
zero when the angle
of the obstacle is
 greater than 60 .
This fuzzy
control
can be
extended
to rear
sensing
by placing
a sensor
at the
back side
of the car
Conclusion:
 The fuzzy logic control system can relieve
  the driver from tension and can prevent
  accidents.
 This fuzzy control unit when fitted in all the
  cars result in an accident free world.
Artificial Neural Networks fuzzy logic

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  • 1. ARTIFICIAL NEURAL NETWORKS FUZZY LOGIC (AUTOMATED AUTOMOBILES)
  • 2. Introduction Fuzzy Logic control system is used to control the speed of the car based on the obstacle sensed. Fuzzy logic is best suited for control applications, such as temperature control, traffic control or process control.
  • 3. Fuzzy Vs. Probability Fuzziness describes the ambiguity of an event. whereas probability describes the uncertainty in the occurrence of the event.
  • 4. Complexity of a System vs. Precision in the model of the System: For systems with little complexity, closed-form mathematical expressions provide precise descriptions of the systems. For systems that are a little more complex, artificial neural networks, provide powerful and robust. For systems with more complex, Fuzzy system is used.
  • 5. Fuzzy Set vs. Crisp Set: A classical set is defined by crisp boundaries; i.e., there is no uncertainty in the prescription or location of the boundaries of the set. A fuzzy set, on the other hand, is prescribed by vague or ambiguous properties.
  • 6. Membership function and features of membership function: Membership function characterize the fuzziness in a fuzzy set. The core comprises those elements X of the universe such that A(x) = 1. The support comprises those elements X of the universe such that A(x) > 0. The boundaries comprise these elements X of the universe such that 0< A(x) <1.
  • 7. Fuzzification and Defuzzification Fuzzification is the process of making a crisp quantity fuzzy. Defuzzification is the conversion of a fuzzy quantity to a precise quantity.
  • 8. Fuzzy Logic Control System Obstacle Sensor Unit: The car consists of a sensor in the front panel to sense the presence of the obstacle.
  • 9. Sensing Distance The sensing distance depends upon the speed of the car and the speed can be controlled by gradual anti skid braking system. The speed of the car is taken as the input and the distance sensed by the sensor is controlled.
  • 10. Input Membership Function: Output Membership Function:
  • 11. The defuzzified values are obtained and the variation of speed with sensing distance is plotted as a surface graph
  • 14. The angle is taken as the input and output speed is controlled.
  • 15. Input Membership Function: Output Membership Function:
  • 16. From the graph it is clear that the speed becomes zero when the angle of the obstacle is greater than 60 .
  • 17. This fuzzy control can be extended to rear sensing by placing a sensor at the back side of the car
  • 18. Conclusion: The fuzzy logic control system can relieve the driver from tension and can prevent accidents. This fuzzy control unit when fitted in all the cars result in an accident free world.