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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