Soft computing is an approach to building computationally intelligent systems that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and evolutionary computation. These techniques were developed to mimic human-like intelligence by accommodating imprecision and exploiting uncertainty. Soft computing is used to build intelligent systems that can learn and adapt to new environments. Neuro-fuzzy systems combine neural networks and fuzzy logic to create adaptive and knowledge-based intelligent systems.

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Introduction to Neuro-fuzzy
and Soft computing

2. What is computing?
Counting, calculating
The discipline of computing is the
systematic study of algorithmic
processes that describe and
transform information: their theory,
analysis, design, efficiency,
implementation, and application.
Types of computing
Hard computing
Soft Computing
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3. Differences between hard and
soft computing
Hard Computing Soft computing
Precisely stated analytical model Tolerant to imprecision,
uncertainty, partial truth,
approximation
Based on binary logic, crisp
systems, numerical analysis, crisp
software
Fuzzy logic, neural nets,
probabilistic reasoning.
Programs are to be written Evolve their own programs
Two values logic Multi valued logic
Exact input data Ambiguous and noisy data
Strictly sequential Parallel computations
Precise answers Approximate answers
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Essence of SC:-
Accommodation
with the pervasive
imprecision of the
real world
Principle of SC:-
Exploit uncertainty
to achieve
robustness and
better rapport with
reality

5. Artificial intelligence
If intelligence can be induced in
machines it is called as artificial
intelligence.
Soft computing is a part of artificial
intelligent techniques
Closed related to machine
intelligence/computational intelligence
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What is Soft computing
Neural Networks
Fuzzy Inference
systems
Neuro-
Fuzzy
Computing
Derivative-
Free
Optimization
Soft Computing
+ =

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Artificial Neural
Networks
Evolutionary
computation
Fuzzy logic
Heuristics
Soft
Computing

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Introduction
SC is an innovative approach to
constructing computationally intelligent
systems
Intelligent systems that possess humanlike
expertise within a specific domain, adapt
themselves and learn to perform better in
changing environments
These systems explain how they make
decisions or take actions
They are composed of two features:
“adaptivity” & “knowledge

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Introduction Contd….
Neural Networks (NN) that recognize
patterns & adapts themselves to cope
with changing environments
Fuzzy inference systems that
incorporate human knowledge &
perform inference & decision making
Adaptivity + Expertise = NF & SC

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What is the difference between Fuzzy Logic and Neural
Networks?
 Fuzzy logic allows making definite decisions
based on imprecise or ambiguous data
 ANN tries to incorporate human thinking process
to solve problems without mathematically
modeling them.
 Both these methods can be used to solve
nonlinear problems, and problems that are not
properly specified, but they are not related.

ANN tries to apply the thinking process in the
human brain to solve problems.

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Latest developments in the field of
soft computing
Areas of image processing
Image retrieval
Image analysis
Remote sensing
Data mining
Swarm intelligence
Diffusion process
Agent’s technology

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Swarm Technology

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SC Constituents and Conventional
AI
“SC 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” [Lotfi A. Zadeh, 1992]
SC consists of several computing paradigms
including:
NN
Fuzzy set theory
Approximate reasoning
Derivative-free optimization methods such as genetic
algorithms (GA) & simulated annealing (SA)

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SC constituents (the first three
items) and conventional AI

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These methodologies form the core of
SC
In general, SC does not perform
much symbolic manipulation
SC in this sense complements
conventional AI approaches

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character recognizer

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Features of Conventional AI
From conventional AI to computational
intelligence
Conventional AI manipulates symbols on the
assumption that human intelligence behavior
can be stored in symbolically structured
knowledge bases: this is known as: “ The
physical symbol system hypothesis
The knowledge-based system (or expert
system) is an example of the most successful
conventional AI product

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What is an expert system?
An expert system is software that
uses a knowledge base of human
expertise for problem solving, or to
clarify uncertainties where
normally one or more
human experts would need to be
consulted

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Expert system

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Building blocks of expert system
Knowledge base: factual knowledge and heuristic
knowledge
Knowledge representation: in the form of rules
Problem solving model: forward chaining or
backward chaining
Knowledge base: knowledge gained by an
individual user
Note:-
Knowledge engineering:- building an expert
system
Knowledge engineers:- practitioners.

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Applications of expert system
1. Diagnosis and Troubleshooting of
Devices and Systems of All Kinds
2. Planning and Scheduling
3. Configuration of Manufactured
Objects from Subassemblies
4. Financial Decision Making
5. Knowledge Publishing
6. Design and Manufacturing

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Several definitions have been given
to conventional AI
“AI is the study of agents that exists in
an environment and perceive and act”
[S. Russel & P. Norvig]
“AI is the act of making computers do
smart things” [Waldrop]
“AI is a programming style, where
programs operate on data according
to rules in order to accomplish goals”
[W.A. Taylor]

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“AI is the activity of providing such
machines as computers with the ability to
display behavior that would be regarded as
intelligent if it were observed in humans”
[R. Mc Leod]
“Expert system is a computer program
using expert knowledge to attain high
levels of performance in a narrow problem
area” [D.A. Waterman]
“Expert system is a caricature of the
human expert, in the sense that it knows
almost everything about almost nothing”
[A.R. Mirzai]
AI is changing rapidly, these definitions are
already obsolete!

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Knowledge acquisition and representation
has limited the application of AI theories
(shortcoming of symbolisms)
SC has become a part of “modern AI”
Researchers have directed their attention
toward biologically inspired methodologies
such as brain modeling, evolutionary
algorithm and immune modeling

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These new paradigms simulate chemico-
biological mechanisms responsible for
natural intelligence generation
SC and AI share the same long-term goal:
build and understand machine intelligence
An intelligent system can for example
sense its environment (perceive) and act
on its perception (react)
SC is evolving under AI influences that
sprang from cybernetics (the study of
information and control in human and
machines)

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Neural Network (NN)
Imitation of the natural intelligence of the brain
Parallel processing with incomplete information
Nerve cells function about 106 times slower than
electronic circuit gates, but human brains process
visual and auditory information much faster than
modern computers
The brain is modeled as a continuous-time non
linear dynamic system in connectionist
architectures • Connectionism replaced
symbolically structured representations
Distributed representation in the form of weights
between a massive set of interconnected neurons

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Fuzzy set theory
Human brains interpret imprecise and incomplete
sensory information provided by perceptive organs
Fuzzy set theory provides a systematic calculus to
deal with such information linguistically
It performs numerical computation by using
linguistic labels stimulated by membership
functions
It lacks the adaptability to deal with changing
external environments ==> incorporate NN
learning concepts in fuzzy inference systems: NF
modeling

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Evolutionary computation
Natural intelligence is the product of
millions of years of biological evolution
Simulation of complex biological
evolutionary processes
GA is one computing technique that uses
an evolution based on natural selection
Immune modeling and artificial life are
similar disciplines based on chemical and
physical laws
GA and SA population-based systematic
random search (RA) techniques

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NF and SC characteristics
With NF modeling as a backbone, SC
can be characterized as:
Human expertise (fuzzy if-then rules)
Biologically inspired computing models
(NN)
New optimization techniques (GA, SA,
RA)
Numerical computation (no symbolic AI
so far, only numerical)

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NF and SC Characteristics Contd…
New application domains: mostly
computation intensive like adaptive
signal processing, adaptive control,
nonlinear system identification etc
Model free learning:-models are
constructed based on the target
system only
Intensive computation: based more
on computation

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NF and SC Characteristics Contd…
Fault tolerance: deletion of a neuron
or a rule does not destroy the system.
The system performs with lesser
quality
Goal driven characteristics:- only the
goal is important and not the path.
Real world application:- large scale,
uncertainties

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summary
SC is evolving rapidly
New techniques and applications are
constantly being proposed

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