This document provides an introduction to soft computing. Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. The key components of soft computing include neural networks, fuzzy logic, and genetic algorithms. The goals of soft computing are to develop intelligent machines to solve real-world problems that may not have ideal or mathematically modeled solutions, while achieving practicality, robustness, and low cost. Soft computing uses techniques like machine learning, evolutionary computation, and artificial neural networks to approach problems that traditional computing cannot always solve.

1. SOFT COMPUTING
Under the guidance of:
Santhosh

2. Introduction
 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 where ideal solutions are not there
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.
 To achieve Practicability, Robustness and low solution
cost.
 Well suited for real world problems

4. • 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.

5. Soft Computing - Development
History

6. Evolving Techniques

7. 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
 Generalization
 Robustness
 Fault Tolerance
 Parallel and High speed information processing

8. 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

9. 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 to 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.

10. Why Soft Computing is Important
 Soft computing provides an approach to problem-solving
using means other than computers.
 With the human mind as a role model, soft computing is
tolerant of partial truths, uncertainty, imprecision and
approximation, unlike traditional computing models.
 The tolerance of soft computing allows researchers to
approach some problems that traditional computing can't
process.

11. Soft Computing uses Component
Fields of Study in:
• Fuzzy logic
• Machine learning
• Probabilistic reasoning
• Evolutionary computation
• Perceptron
• Genetic algorithms
• Differential algorithms
• Support vector machines
• Metaheuristics
• Swarm intelligence
• Ant colony optimization
• Particle optimization
• Bayesian networks
• Artificial neural networks
• Expert systems