soft computing

2.  “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”.
 Soft Computing is the fusion of methodologies designed
to model and enable solutions to real world problems,
which are not modeled or too difficult to model
mathematically.
 The aim of Soft Computing is to exploit the tolerance for
imprecision, uncertainty, approximate reasoning, and
partial truth in order to achieve close resemblance with
human like decision making.
The idea of soft computing was initiated in 1981 BY
Lotfi A. Zadeh.

3.  Soft Computing is a term used in computer
science to refer to problems in whose solutions
are unpredictable, uncertain and between 0
and 1.
 Soft computing deals with imprecision,
uncertainty, partial truth, and approximation to
achieve practicability, robustness and low
solution cost.
 Soft Computing is one multidisciplinary system
as the fusion of the fields of Fuzzy Logic,
Neuro-Computing, Evolutionary and Genetic
Computing, and Probabilistic Computing.

4.  To develop intelligent machines to provide
solutions to real world problems, which are not
modeled, or too difficult to model mathematically.
 To exploit the tolerance for Approximation,
Uncertainty, Imprecision, and Partial Truth in order
to achieve close resemblance with human like
decision making.
 Well suited for real world problems where ideal
solutions are not there.

5.  Models based on human reasoning
 Closer to human thinking.
 Models can be:-
 linguistic
 It involves human like thinking.
 They handle noisy or missing data.
 They can work with large number of variables or
parameters.
 They provide general solutions with good predictive
accuracy.
 System has got property of continuous learning.

6.  Approximation : here the model features are similar to the
real ones, but not the same.
 Uncertainty : here we are not sure that the features of the
model are the same as that of the entity (belief).
 Imprecision : here the model features (quantities) are not
the same as that of the real ones, but close to them.
achieve tractability, robustness and low
solution cost.

7. Hard computing Soft Computing
requires
precisely state analytic mode
l
tolerant of
imprecision, uncertainty, partial
truth and approximation
based on binary logic, crisp
system, numerical analysis
and crisp software
based on fuzzy logic, neural
sets,
and probabilistic reasoning
requires programs to be
written
can evolve its own programs
uses two-valued logic. can use multivalued or fuzzy
logic
requires exact input data. can deal with ambiguous and
noisy data
produces precise answers. can yield approximate
answers

8.  Fuzzy Computing
Multivalued Logic for treatment of imprecision and
vagueness.
 Neural Computing
Neural Computers mimic certain processing
capabilities of the human brain.
 Genetic Algorithms
Genetic Algorithms (GAs) are used to mimic some of
the processes observed in natural evolution and GAs
are used to evolve programs to perform certain tasks.
This method is known as "Genetic Programming"
(GP).

9. 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.

10.  Conventional AI mostly involves methods now classified as
machine learning, characterized by formalism and statistical
analysis. This is also known as symbolic AI, logical AI, or neat AI.
Methods include:
 Expert systems: applies reasoning capabilities to reach a
conclusion. An expert system can process large amounts of
known information and provide conclusions based on them.
 Case-based reasoning is the process of solving new problems
based on the solutions of similar past problems.
 Bayesian networks represents a set of variables together with a
joint probability distribution with explicit independence
assumptions.
 Behavior-based AI: a modular method of building AI systems by
hand.

11. Computational Intelligence involves iterative development or learning.
Learning is based on empirical data. It is also known as non-symbolic AI,
scruffy AI, and soft computing. Methods mainly include:
 Neural networks: systems with very strong pattern recognition
capabilities.
 Fuzzy systems: techniques for reasoning under uncertainty, have been
widely used in modern industrial and consumer product control systems.
 Evolutionary computation: applies biologically inspired concepts such as
populations, mutation, and survival of the fittest to generate increasingly
better solutions to the problem. These methods most notably divide into
evolutionary algorithms and swarm intelligence.
 Hybrid intelligent systems attempt to combine these two groups. It is
thought that the human brain uses multiple techniques to both formulate
and cross-check results. Thus, systems integration is seen as promising
and perhaps necessary for true AI.

12.  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.

13. http://www.sciencedirect.com/science/articl
e/pii/S0957417407003946
http://www.slideshare.net/DEEPASHRIHK/
artificial-neural-network-
18449085?related=1
Neural Networks, Fuzzy Logic, and
Genetic Algorithms Synthesis and
Application by S. Rajasekaran and G.A.
Vijayalakshmi Patel.