SOFT COMPUTING

PRESENTED BY:

GANESH PAUL

TT – IT(02)

What is Soft Computing?Soft computing is an emerging approach to

computing which parallel the remarkable ability of the human mind to reason and learn in a environment of uncertainty and imprecision.

Some of it’s principle components includes:Neural Network(NN)Fuzzy Logic(FL)Genetic Algorithm(GA)These methodologies form the core of soft

computing.

GOALS OF SOFT COMPUTINGThe main goal of soft computing is to develop

intelligent machines to provide solutions to real world problems, which are not modeled, or too difficult to model mathematically.

It’s aim is to exploit the tolerance for Approximation, Uncertainty, Imprecision, and Partial Truth in order to achieve close resemblance with human like decision making.

SOFT COMPUTING -DEVELOPMENT HISTORY

Soft = Evolutionary + Neural + FuzzyComputing Computing Network LogicZadeh Rechenberg McCulloch Zadeh1981 1960 1943 1965

Evolutionary = Genetic + Evolution + Evolutionary + Genetic

Computing Programming Strategies programming Algorithms

Rechenberg Koza Rechenberg Fogel Holland

1960 1992 1965 1962 1970

NEURAL NETWORKSAn NN, in general, is a highly interconnected

network of a large number of processing elements called neurons in an architecture inspired by the brain.

NN Characteristics are:-Mapping Capabilities / Pattern AssociationGeneralisationRobustnessFault ToleranceParallel and High speed information

processing

6

Neuron

Biological neuron

Model of a neuron

ANN ARCHITECTURES

Input Layer Output Layer1.Single Layer Feedforward Network

Input Layer Hidden Layer Output Layer2.Multilayer Feedforward Network

Input Layer Hidden Layer Output Layer3.Recurrent Networks

Xi - Input Neuron

Yi - Hidden /Output Neuron

Zi - Output Neuron

i = 1,2,3,4…..

X1

X2

X3

y1

y2

y3

X1

X2

X3

y1

y2

z1

z2

z3

X1

X2

X3

y1

y2

z1

z2

z3

LEARNING METHODS OF ANN

NN Learning algorithms

SSupervised

Learning

UnsupervisedLearning

ReinforcedLearning

ErrorCorrecti

on

Stochastic Hebbian Competitiv

e

Least Mean

Square

Backpropagation

FUZZY LOGICFuzzy set theory proposed in 1965 by A. Zadeh

is a generalization of classical set theory.In classical set theory, an element either belong

to or does not belong to a set and hence, such set are termed as crisp set. But in fuzzy set, many degrees of membership (between o/1) are allowed

FUZZY VERSES CRISPFUZZY CRISPIS R AM HONEST ? IS WATER COLORLESS ?

FUZZY CRISP

ExtremelyHonest(1)

Very Honest(0.8)

Honest atTimes(0.4)

ExtremelyDishonest(

0)

YES!(1)

NO!(0)

OPERTIONSCRISP FUZZY

1.Union2.Intersection3.Complement4.Difference

1.Union2.Intersection3.Complement4.Equality5.Difference6.Disjunctive Sum

PROPERTIESCRISP FUZZYCommutativityAssociativityDistributivityIdempotenceIdentityLaw Of AbsorptionTransitivityInvolutionDe Morgan’s LawLaw Of the Excluded

MiddleLaw Of Contradiction

CommutativityAssociativityDistributivityIdempotenceIdentityLaw Of AbsorptionTransitivityInvolutionDe Morgan’s Law

GENETIC ALGORITHMGenetic Algorithms initiated and developed in

the early 1970’s by John Holland are unorthodox search and optimization algorithms, which mimic some of the process of natural evolution. Gas perform directed random search through a given set of alternative with the aim of finding the best alternative with respect tp the given criteria of goodness. These criteria are required to be expressed in terms of an object function which is usually referred to as a fitness function.

BIOLOGICAL BACKGROUNDAll living organism consist of cell. In each cell,

there is a set of chromosomes which are strings of DNA and serves as a model of the organism. A chromosomes consist of genes of blocks of DNA. Each gene encodes a particular pattern. Basically, it can be said that each gene encodes a traits.

Fig.Genome consistingOf chromosomes.

A

T

G

C

T

AG

C

A

G

T

A

C

ENCODINGThere are many ways of representing individual

genes.

Binary EncodingOctal EncodingHexadecimal EncodingPermutation EncodingValue EncodingTree Encoding

BENEFITS OF GENETIC ALGORITHMEasy to understand.We always get an answer and the answer gets

better with time.Good for noisy environment.Flexible in forming building blocks for hybrid

application.Has substantial history and range of use.Supports multi-objective optimization.Modular, separate from application.

APPLICATION OF SOFT COMPUTINGConsumer appliance like AC, Refrigerators,

Heaters, Washing machine.Robotics like Emotional Pet robots.Food preparation appliances like Rice

cookers and Microwave.Game playing like Poker, checker etc.

FUTURE SCOPESoft Computing can be extended to include

bio- informatics aspects.Fuzzy system can be applied to the

construction of more advanced intelligent industrial systems.

Soft computing is very effective when it’s applied to real world problems that are not able to solved by traditional hard computing.

Soft computing enables industrial to be innovative due to the characteristics of soft computing: tractability, low cost and high machine intelligent quotient.

REFERENCES Neural Networks, Fuzzy Logic, and Genetic Algorithms

Synthesis and Application by S. Rajasekaran and G.A. Vijayalakshmi Patel

L. A. Zadeh, “Fuzzy logic, neural networks and soft computing,” in Proc. IEEE Int. Workshop Neuro Fuzzy Control, Muroran, Japan, 1993.

T. Nitta, “Application of neural networks to home appliances,” in Proc. IEEE Int. Joint Conf. Neural Networks, Nagoya, Japan, 1993.

P.J. Werbos, “Neuro-control and elastic fuzzy logic: Capabilities, concepts and application,” IEEE Trans. Ind. Electron., Vol. 40. 1993.

Y. Dote and R.G. Hoft, Intelligent Control-Power Electronics Systems. Oxford, U.K.: Oxford Univ. Press, 1998.

L. A. Zadeh, “From computing with numbers to computing with words-From manipulation of measurements to manipulation of perception,” IEEE Trans. Circuit Syst., Vol. 45, Jan 1999.

Any Questions