Shiva Shrestha

HSM, Hetauda

21 December 2014 1SHIVA SHRESTHA, HSM, Hetauda

Fuzzy sets

Fuzzy logic

Judgmental bias

corrective procedures

Creativity concepts and approaches

21 December 2014SHIVA SHRESTHA, HSM, Hetauda 2

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.

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.

21 December 2014 5SHIVA SHRESTHA, HSM, Hetauda

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.

21 December 2014 6SHIVA SHRESTHA, HSM, Hetauda

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.

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

21 December 2014 8SHIVA SHRESTHA, HSM, Hetauda

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

21 December 2014 9SHIVA SHRESTHA, HSM, Hetauda

BENEFITS OF USING FUZZY LOGIC

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

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

21 December 2014 14SHIVA SHRESTHA, HSM, Hetauda

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..

21 December 2014 15SHIVA SHRESTHA, HSM, Hetauda

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.

21 December 2014 16SHIVA SHRESTHA, HSM, Hetauda

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.

Thank you very much!!!

21 December 2014SHIVA SHRESTHA, HSM, Hetauda 18