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Introduction
Basics of Soft Computing
Soft Computing
Introduction to Soft Computing, topics : Definitions, goals, and
set,
capturing
computing : classical set theory, crisp & non-crisp
uncertainty,
definition
of
fuzzy
set,
in
importance. Fuzzy
graphic
empty.
Fuzzy
operations
:
e.
interpretations of fuzzy set - small, prime numbers, universal space,
inclusion,
equality,
comparability,
in neural cell. Artificial neuron - functions, equation,
et
information flow
ub
complement, union, intersection. Neural computing : biological model,
mechanics
cs
elements, single and multi layer perceptrons. Genetic Algorithms :
of biological evolution, taxonomy of artificial evolution &
techniques,
evolutionary
ww
search
w.
search optimization - enumerative, calculus-based and guided random
memory : description of AM,
algorithms
examples of
(EAs).
Associative
auto and
hetero AM.
Adaptive Resonance Theory : definitions of ART and other types
of learning, ART description, model functions, training, and systems.
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Introduction
Basics of Soft Computing
Soft Computing
Topics
(Lectures 01, 02, 03, 04, 05, 06
6 hours)
Slides
03-06
1. Introduction
What is Soft computing?
Definitions, Goals, and Importance.
07-28
2. Fuzzy Computing
Fuzzy Set: Classical set theory, Crisp & Non-crisp set, Capturing
uncertainty, Definition of fuzzy set; Graphic Interpretations : Fuzzy set Small, Prime numbers, Universal space, Empty; Fuzzy operations :
Complement, Union, Intersection.
in
Inclusion, Equality, Comparability,
29-39
e.
3. Neural Computing
ub
Biological model, Information flow in neural cell; Artificial neuron:
et
Functions, Equation, Elements, Single and Multi layer Perceptrons.
40-49
cs
4. Genetic Algorithms
w.
What are GAs ? Why GAs ? Mechanics of Biological evolution; Artificial
ww
Evolution and Search Optimization: Taxonomy of Evolution & Search
optimization - Enumerative,
Calculus-based
and
Guided random
search techniques, Evolutionary algorithms (EAs).
50-53
5. Associative Memory
Description of AM; Examples of Auto and Hetero AM.
54-58
6. Adaptive Resonance Theory
Definitions of ART and other types of learning; ART : Description,
Model functions , Training, and Systems.
7. Applications of Soft Computing
59
60-61
8. References
02
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Introduction
Basics of Soft Computing
What is Soft Computing ?
• The idea of soft computing was initiated in 1981 when Lotfi A. Zadeh
published his first paper on soft data analysis
“What is Soft Computing”,
Soft Computing. Springer-Verlag Germany/USA 1997.].
• Zadeh, defined Soft Computing into one multidisciplinary system as the
fusion of
the fields of Fuzzy Logic, Neuro-Computing, Evolutionary and
Genetic Computing, and Probabilistic Computing.
in
• Soft Computing is the fusion of methodologies designed to model and
difficult to model mathematically.
e.
enable solutions to real world problems, which are not modeled or too
ub
• The aim of Soft Computing is to exploit the tolerance for imprecision,
in order to achieve
et
uncertainty, approximate reasoning, and partial truth
cs
close resemblance with human like decision making.
=
ww
SC
Soft
Computing
Zadeh
1981
EC
w.
• The Soft Computing – development history
=
EC
+
NN
+
FL
Evolutionary
Computing
Neural
Network
Fuzzy
Logic
Rechenberg
1960
McCulloch
1943
Zadeh
1965
GP
+
ES
+
EP
+
GA
Evolutionary
Computing
Genetic
Programming
Evolution
Strategies
Evolutionary
Programming
Genetic
Algorithms
Rechenberg
1960
Koza
1992
Rechenberg
1965
Fogel
1962
Holland
1970
03
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SC - Definitions
1. Introduction
To begin, first explained, the definitions, the goals, and the importance of
the soft computing. Later, presented its different fields, that is, Fuzzy
Computing, Neural Computing, Genetic Algorithms, and more.
Definitions of Soft Computing (SC)
Lotfi
A.
Zadeh,
1992
:
“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”.
The
Soft
Computing
consists
of
several
computing
paradigms
mainly :
Fuzzy Systems, Neural Networks, and Genetic Algorithms.
in
• Fuzzy set : for knowledge representation via fuzzy If – Then rules.
e.
• Neural Networks : for learning and adaptation
ub
• Genetic Algorithms : for evolutionary computation
et
These methodologies form the core of SC.
cs
Hybridization of these three creates a successful synergic effect;
that is, hybridization creates a situation where different entities cooperate
ww
w.
advantageously for a final outcome.
Soft Computing is still growing and developing.
Hence, a clear definite agreement on what comprises Soft Computing has
not yet been reached. More new sciences are still merging into Soft Computing.
04
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