fuzzy logic by saurav garg

7/28/2019 fuzzy logic by saurav garg

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FUZZY LOGICdatabase

Submitted by:

Saurabh Kumar Garg

B.Tech IT VIII Sem

09EUDIT047


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Index

Brief HistoryIntroduction

What is fuzzy logic?

Why use Fuzzy Logic? Fuzzy Logic in Database

Fuzzy Logic VS. Probability

Fuzzy ApplicationsConclusion

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Brief History

Classical logic of Aristotle: Law of Bivalence Everyproposition is either True or False(no middle)

Jan Lukasiewicz proposed three-valued logic : True,False and Possible

Finally Lofti Zadeh published his paper on fuzzy logic-apart of set theory that operated over the range [0.0-1.0]

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Introduction

In many real world Applications Such as business decision

making, medical diagnosis, and criminal justice, have to deal

with information that is uncertain or imprecise .

Classical database models often suffer from their incapability

of representing and manipulating imprecise and uncertain

information.

Example :

The age of Tom is About 32 .

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The fuzzy logic has been used to extend various database

models.

The purpose of introducing fuzzy logic in databases is to

enhance the classical database models such that uncertain andimprecise information can be represented and manipulated.

In recent years, various fuzzy data models and fuzzy database

systems have been proposed.

These models and systems extend relational and object-

Oriented data models using the fuzzy set and the possibility

theory to provide the ability of representing ill-known data

and issuing queries containing Soft restrictions

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WHAT IS FUZZY LOGIC?

Definition of fuzzy

Fuzzynot 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.

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Why use Fuzzy Logic?

Fuzzy logic is flexible.

Fuzzy logic is conceptually easy to

understand.

Fuzzy logic is tolerant of imprecise data.

Fuzzy logic is based on natural language.

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Fuzzy concepts in database

`Fuzzy' information or `fuzzy' data can arise in several ways.

It could be :

1.Due to subjective judgments.

i.e. a database may contain data that is describing things such as

the quality of a school, the safety of a neighborhoods etc)

2.Due to nature of the information required by a user .

i.e. a user may wish to make imprecise queries (`I want a list of

universities that

offer a good graduate program in software engineering and

where the cost of

living is low')

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An example:

By using conventional method we can call a person TALL if

the height is 7 feet and a person with height 5 feet is NOT

TALL. That is we represent the person is either TALL or

NOT TALL in Boolean Logic 1 or 0, 1 for TALL and 0 for

NOT TALLFuzzy sets may be used to show the relationship or

degree of precision:

If S is the set of all people in the Universe, a degree of

membership is assigned to each person in set S to find thesubset TALL.

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Degree of relationship11


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Fuzzy Logic Vs Probability

Both operate over the same numeric range and at firstglance both have similar values:0.0 representing false(ornon-membership) and 1.0 representing true.

In terms of probability, the natural language statementwould be there is an 80% chance that Jane is old.

While the fuzzy terminology corresponds to Janesdegree of membership within the set of old people is 0.80.

Fuzzy logic uses truth degrees as a mathematicalmodel of the vagueness phenomenon while probability isa mathematical model of ignorance.

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Fuzzy Applications

Automobile and other vehicle subsystems : used to control

the speed of vehicles, in Anti Braking System.

Temperature controllers : Air conditioners, Refrigerators

Cameras : for auto-focus

Home appliances: Rice cookers , Dishwashers , Washing

machines and others

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Fuzzy logic is not always accurate. The results are perceived as

a guess, so it may not be as widely trusted .

Fuzzy Logic control may not scale well to large or complex

problems

Fuzzy logic can be easily confused with probability theory, and

the terms used interchangeably. While they are similar concepts,they do not say the same things.

Drawbacks14


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THANKS YOU

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fuzzy logic by saurav garg

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