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Introduction to soft computin

Nirmala Shinde

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Syllabus

• Introduction to soft Computing• Fuzzy Set Theory

• Fuzzy Systems

• Hybrid System

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Books

• Principle of Soft computing:, sivanandam, wiley• Neural Network, fuzzy logic, and genetic algorithm, Rajasekaran

hall

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!ontent

•  "I # Soft computing• $rom conventional "I to !omputational Intelligence

• %hat is soft computing&

• roblem Solving 'echni(ues

• )ard *s Soft !omputing

• +verview of techni(ues in soft computing

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 "I and Softcomputing

•  "I predicate logic and symbol manipulation techni(ues

   -  s  e  r   I  n   t  e  r   f

  a  c  e

Inference

.ngine

./planation

$acility

0nowledge

 "c(uisition

0B•$act•rules

1lobal

2atabase

0nowledge

.ngineer 

)uman

./pert

3uestion

Response

./pert Systems

-ser 

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 "I and Softcomputing

 "NN

4earning and

adaptation

$u55y Set 'heory

0nowledge representation

*ia

$u55y if6then R-4.

1enetic "lgorithms

Systematic

Random Search

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 "I and Softcomputing

 "NN

4earning and

adaptation

$u55y Set 'heory

0nowledge representation

*ia

$u55y if6then R-4.

1enetic "lgorithms

Systematic

Random Search

 "I

Symbolic

7anipulation

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 "I and Softcomputing

cat

cut

knowledge

 "nimal& ca

Neural character 

recognition

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$rom !onventional "I to !omputational

Intelligence

• !onventional "I• $ocuses on attempt to mimic human intelligent behavior by e/pressing itforms or symbolic rules

• 7anipulates symbols on the assumption that such behavior can be storesymbolically structured knowledge bases 8physical symbol system hypot

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$rom !onventional "I to !omputational

Intelligence

• Intelligent Systems

Sensing 2evices

8*ision9

Natural

4anguage

rocessor 

7echanical

2evices

erceptions

 "ctions

'ask

1enerator 

0nowledge

)andler 

2ata

)andler  0nowledge

Base

7achine

4earning

Inferencing

8Reasoning

lanning

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%hat is soft computing &

• :Soft !omputing is an emerging approach to computing which premarkable ability of the human mind to reason and learn in a

environment of uncertainty and imprecision;<

• Soft !omputing is the fusion of methodologies designed to mod

enable solutions to real world problems, which are not modeled

difficult to model mathematically.

• 'he aim of Soft !omputing is to e/ploit the tolerance for imprec

uncertainty, approximate reasoning, and partial truth in orde

achieve close resemblance with human like decision making<

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!ont=

• Soft !omputing is a term used in computer science to refer to prwhose solutions are unpredictable, uncertain and between > and

• Soft computing deals with imprecision, uncertainty, partial truth,

appro/imation to achieve practicability, robustness and low s

cost.

• 'he idea of soft computing was initiated in ?@A? B 4otfi "< Ca

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!ont=

ccording to !rof. "adeh#

"...in contrast to traditional hard computing, soft computing exploitolerance for imprecision, uncertainty, and partial truth to achievtractaility, roustness, low solution!cost, and etter rapport wit

en.wikipedia.org/wiki/Soft_computing  :

Soft computing is a term applied to a field within computer scien

characterized y the use of inexact solutions to computationally

such as the solution of NP!complete prolems, for which an exa

cannot e derived in polynomial time.

http://www.google.co.in/url?q=http://en.wikipedia.org/wiki/Soft_computing&sa=X&ei=B4hXTZHqG8GxrAeytZzGBw&ved=0CAgQpAMoAA&usg=AFQjCNG3bwKE5IbElaUSCQRQGqBUWPtAEghttp://www.google.co.in/url?q=http://en.wikipedia.org/wiki/Soft_computing&sa=X&ei=B4hXTZHqG8GxrAeytZzGBw&ved=0CAgQpAMoAA&usg=AFQjCNG3bwKE5IbElaUSCQRQGqBUWPtAEghttp://www.google.co.in/url?q=http://en.wikipedia.org/wiki/Soft_computing&sa=X&ei=B4hXTZHqG8GxrAeytZzGBw&ved=0CAgQpAMoAA&usg=AFQjCNG3bwKE5IbElaUSCQRQGqBUWPtAEghttp://www.google.co.in/url?q=http://en.wikipedia.org/wiki/Soft_computing&sa=X&ei=B4hXTZHqG8GxrAeytZzGBw&ved=0CAgQpAMoAA&usg=AFQjCNG3bwKE5IbElaUSCQRQGqBUWPtAEg

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1oals of Soft !omputing

• 'o develop intelligent machines to provide solutions to real worldproblems, which are not modelled or too difficult to model mathe

• 'o e/ploit the tolerance for appro/imation, uncertainty, imprecisi

partial truth in order to achieve close resemblance with human li

decision making<

• %ell suited for real world problems where ideal solutions are not

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!ont=

• imprecision D the model features 8(uantities9 are not the same a

the real ones, but close to them<• uncertainty D we are not sure that the features of the model are

as that of the entity 8belief9<

•  "ppro/imate Reasoning D the model features are similar to the

but not the same<

• 'he guiding principle of soft computing is to e/ploit these toleraachieve tractability, robustness and low solution cost.

• 'he role model for soft computing is the human mind<

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roblem Solving 'echni(ues

SymbolicLogic

Reasoning

 TraditionalNumerical Modeling

and Search

Approximate

Reasoning 

FunctionalApproximatio

and RandomizeSearch

HARD COMPUTING SOFT COMPUTING

Precise Models Approximate Models

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)ard !omputing *s Soft !omputing

Hard computing Soft Computingre(uires precisely stateanalytic model

tolerant ofimprecision, uncertainty, partialtruth and appro/imation

based on binary logic, crispsystem, numerical analysis

and crisp software

based on fu55y logic, neuralsets,

and probabilistic reasoning

has the characteristics ofprecision

has the characteristics ofappro/imation

re(uires programs to bewritten

can evolve its own programs

 uses two6valued logic< can use multivalued or fu55y

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+verview of techni(ues in soft computing

•Neural Network

• $u55y 4ogic

• 1enetic "lgorithm

• )ybrid Systems

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Neural Network

2"R" Neural Network Study 8?@AA, "$!." International ress,

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!ont=

 "ccording to )aykin 8?@@F9, p< G " neural network is a massively parallel distributed processor that

natural propensity for storing e/periential knowledge and making

for use< It resembles the brain in two respects

• 0nowledge is ac(uired by the network through a learning proces

• Interneuron connection strengths known as synaptic weights are

store the knowledge

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!ont=

ccording to $igrin %&''(), p. &

 " neural network is a circuit composed of a very large number of s

processing elements that are neurally based< .ach element operat

local information<

$urthermore each element operates asynchronouslyH thus there is

system clock<

ccording to "urada %&''*)#

 "rtificial neural systems, or neural networks, are physical cellular s

which can ac(uire, store and utili5e e/periential knowledge<

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7ultidisciplinary view of neural network

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$u55y 4ogic

• +rigins 7ultivalued 4ogic for treatment of imprecision and vagu

• ?@>s ost, 0leene, and 4ukasiewic5 attempted to represent

undetermined, unknown, and other possible intermediate truth6v

• ?@J 7a/ Black suggested the use of a consistency profile to revague 8ambiguous9 concepts<

• ?@EK Cadeh proposed a complete theory of fu55y sets 8and its i

fu55y logic9, to represent and manipulate ill6defined concepts<

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1enetic "lgorithm

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2efinition of 1enetic "lgorithm

• 'he genetic algorithm is a probabalistic search algorithm that ite

transforms a set 8called a population9 of mathematical objects 8t

fi/ed6length binary character strings9, each with an associated fi

value, into a new population of offspring objects using the 2arwi

principle of natural selection and using operations that are patte

naturally occurring genetic operations, such as crossover 8se/ua

recombination9 and mutation<

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Steps Involved in 1enetic "lgorithm

• 'he genetic algorithms follow the evolution process in the nature to

better solutions of some complicated problems< $oundations of genalgorithms are given in )olland 8?@JK9 and 1oldberg 8?@A@9 books

• 1enetic algorithms consist the following steps

• Initiali5ation

• Selection

• Reproduction with crossover and mutation

• Selection and reproduction are repeated for each generation until a

reached<

• 2uring this procedure a certain strings of symbols, known as chrom

evaluate toward better solution<

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)ybrid Systems

• )ybrid systems enables one to combine various soft computing

and result in a best solution< 'he major three hybrid systems are

follows

• )ybrid $u55y 4ogic 8$49 Systems

• )ybrid Neural Network 8NN9 Systems

• )ybrid .volutionary "lgorithm 8."9 Systems

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Neural Networks