1. Soft computing is a branch of artificial intelligence that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and probabilistic reasoning. 2. Soft computing is used to build intelligent systems that exhibit traits of adaptability and knowledge. It combines neural networks, which recognize patterns and adapt, with fuzzy inference systems, which incorporate human knowledge for decision making. 3. Recent developments in soft computing include applications in image processing, remote sensing, and data mining techniques like swarm intelligence and diffusion processes.

1. 1
Introduction to Neuro-fuzzy
and Soft computing
Dr. Prafull Chandra Narooka
Department of Computer Science
School of Engineering & Systems
Sciences
MDS University, Ajmer

2. What is computing?
2
 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

3. Differences between hard and soft computing
3
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

4.  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
4

5. Artificial intelligence
5
 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

6. What is Soft computing
6
Neural Networks
Fuzzy Inference
systems
Neuro-
Fuzzy
Computing
Derivative-
Free
Optimization
Soft Computing
+ =

7. What is Soft computing
7
Artificial Neural
Networks
Evolutionary
computation
Fuzzy logic
Heuristics
Soft
Computing

8. Introduction
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 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

9. Introduction Contd….
9
 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

10. What is the difference between Fuzzy Logic and Neural Networks?
10
 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.

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