Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.

1. PRESENTED BY:
GANESH PAUL
TT – IT(02)

2. 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.

3. GOALS OF SOFT COMPUTING
The 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.

4. SOFT COMPUTING -
DEVELOPMENT HISTORY
Soft = Evolutionary + Neural + Fuzzy
Computing Computing Network Logic
Zadeh Rechenberg McCulloch Zadeh
1981 1960 1943 1965
Evolutionary = Genetic + Evolution + Evolutionary + Genetic
Computing Programming Strategies programming Algorithms
Rechenberg Koza Rechenberg Fogel Holland
1960 1992 1965 1962 1970

5. NEURAL NETWORKS
An 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 Association
Generalisation
Robustness
Fault Tolerance
Parallel and High speed information processing

6. Neuron
Biological neuron
Model of a neuron
6

7. ANN ARCHITECTURES
X1 y1 X1 z1
y1
X2 y2
X2
z2
y2
X3 y3
X3 z3
Input Layer Output Layer Input Layer Hidden Layer Output Layer
1.Single Layer Feedforward Network 2.Multilayer Feedforward Network
Xi - Input Neuron
X1 z1
y1 Yi - Hidden /Output Neuron
X2 z2
y2
z3
Zi - Output Neuron
X3
Input Layer Hidden Layer Output Layer i = 1,2,3,4…..
3.Recurrent Networks

8. LEARNING METHODS OF ANN
NN Learning
algorithms
SSupervised Unsupervised Reinforced
Learning Learning Learning
Error
Correction Stochastic Hebbian Competitive
Least Mean
Square Backpropagation

9. FUZZY LOGIC
Fuzzy 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

10. FUZZY VERSES CRISP
FUZZY CRISP
IS R AM HONEST ?
IS WATER COLORLESS ?
Extremely
Honest(1) YES!(1)
Very
FUZZY Honest(0.8) CRISP
Honest at
Times(0.4) NO!(0)
Extremely
Dishonest(0)

11. OPERTIONS
CRISP FUZZY
1.Union 1.Union
2.Intersection 2.Intersection
3.Complement 3.Complement
4.Difference 4.Equality
5.Difference
6.Disjunctive Sum

12. PROPERTIES
CRISP FUZZY
 Commutativity  Commutativity
 Associativity  Associativity
 Distributivity  Distributivity
 Idempotence  Idempotence
 Identity  Identity
 Law Of Absorption  Law Of Absorption
 Transitivity  Transitivity
 Involution  Involution
 De Morgan’s Law  De Morgan’s Law
 Law Of the Excluded Middle
 Law Of Contradiction

13. GENETIC ALGORITHM
Genetic 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.

14. BIOLOGICAL BACKGROUND
All 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.
A
Fig. A
G
G C
Genome T
consisting A
A
Of T C
G
T C
chromosomes.

15. ENCODING
There are many ways of representing individual genes.
Binary Encoding
Octal Encoding
Hexadecimal Encoding
Permutation Encoding
Value Encoding
Tree Encoding

16. BENEFITS OF GENETIC ALGORITHM
Easy 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.

17. APPLICATION OF SOFT
COMPUTING
Consumer 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.

18. FUTURE SCOPE
Soft 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.

19. 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.

20. Any Questions