Fuzzy Logic 3547

8/7/2019 Fuzzy Logic 3547

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FUZZY LOGIC CONTROL SYSTEMARTIFICIAL NEURAL NETWORKS

WELCOME


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PresentedbyK.SASIKANTH S.PHANI SURESH K.SIVA SANDEEP

OFB.TECH III YEAR

IN

COMPUTER SCIENCE STREAMGODAVARI INSTITUTE OF ENGG &TECHNOLOGY


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Introduction

Fuzzy logic is best suited for control applications

The ability to embed imprecise human reasoning and complex problems

is the criterion by which the efficiency of fuzzy logic is judged. Fuzziness describes the ambiguity of an event. But not the uncertainty

in the randomness


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Complexity of a System vs. Precision in the System model With little complexity, hence little uncertainty, closed-form mathematical

expressions provide precise descriptions of the systems.

In little more complex systems as artificial neural networks, provide a powerfuland robust means to reduce some uncertainty through learning,.

the most complex systems possess imprecise information where fuzzy reasoningallows us to interpolate approximately between observed I/P & O/P situations.


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Fuzzy Set vs. Crisp Set

& Aclassical set is defined by crisp boundaries.& A fuzzy set, on the other hand, is prescribed by

ambiguous properties resulting in ambiguous

boundaries


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Membership Function & its features

characterizes the fuzziness in a fuzzy set

whether the elements in the set are discrete or continuous - in agraphical form for eventual use in the mathematical formalisms offuzzy set theory.

The core of a membership function Q(x) =

1. The support is given by QA(x)>0.

Boundaries are given by 0 < QA (x)


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Fuzzification

Fuzzification is the process of making a crisp quantity fuzzy.

They carry considerable uncertainty.

If the form of uncertainty arises because of imprecision orfuzziness, it can be represented by a membership function.

institution method is used for fuzzification of the input variables.


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DefuzzificationDefuzzification is the conversion of a fuzzy quantity to a precisequantity.

Defuzzification techniques :

1. Max - Membership Principle:

known as height method is limited to peaked output junctions. Givenby

Qc (Z*) u Q (Z) for all z C

2. CentroidMethod:

also called center of area, center of gravity given by

Z* = (Z)dzC(Z).zdzC

~

~


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3. WeightedAverageMethod:& Its valid for symmetrical O/P membership function. Given by

Z* = where 7 denotes an algebraic sum.

4. Means-MaxMembership: ( middle of maxima )

& TheMAXmembership can be a plateau rather than a single point. Givenby

Z* =

)z(c

z).z(c

2

ba


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Obstacle Sensor Unit

SensingDistance:

The sensing distance depends upon the speed of the car. speed canbe controlled by gradual anti skid braking system.

InputMembership Function:

OutputMembership Function:

Fuzzy logic control system


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The defuzzified values are obtained and the variation of speedwith sensing distance is plotted as a surface graph using mat lab


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Speed Control

The speed of the car is controlled according to the angle subtended bythe obstacle.

if the obstacle subtends an angle less than 60r, the car overcomes it &

speed q

1. Speed breaker

2. Fly over


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Obstacles which the carcannot overcome

The angle is taken as the i/p &the o/p speed is controlled.

Input - Membership Function:

Output - MembershipFunction:

The rules are applicable notonly for obstacles that haveelevation but also depressionlike a small pit, subway, etc.


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Using matlab the surface graph relating the speed and angle isobtained.


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Conclusion

& An automated accident prevention system is necessary to preventaccidents.

& The fuzzy logic control system can relieve the driver from tension &

prevents accidents.& This fuzzy control unit results in an accident free world.


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AN ACCIDENT FREE WORLD

THANK YOU


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Any Questions???

Documents

Fuzzy Logic 3547