1. Soft Computing
Introduction

2. Introduction
Soft Computing refers to a consortium of computational methodologies like fuzzy logic, neural networks, genetic algorithms etc
All having their roots in the Artificial Intelligence
Artificial Intelligence is an area of computer science concerned with designing intelligent computer systems.
Systems that exhibit the characteristics we associate with intelligence in human behavior.

3. Soft Computing was introduced by Lotfi A zadeh of the university of California, Berkley, U.S.A
The soft computing differs from hard computing in its tolerance to imprecision, uncertainty and partial truth.
Soft Computing has high Machine Intelligent Quotient [MIQ]
It is the processes of analyzing, organizing and converting data into knowledge is defined as the structured information acquired and applied to remove ignorance and uncertainty about a specific task pertaining to the intelligent machine.

4. Hybrid Systems x Neuro Fuzzy y Neuro Genetic Algorithms
z Genetic Algorithms Fuzzy
k Neuro Fuzzy Genetic Algorithms
Neural Networks
Genetic Algorithms y k x z
Fuzzy Logic

5. Neural Networks
It is simplified models of the biological nervous system and therefore have drawn their motivation from the kind of computing performed by a human brain.
It is as highly interconnected networks of a large number of processing elements called neurons is an architecture inspired by the brain.
It can be massively parallel , so it exhibits parallel distributed processing.
Neural networks learn by examples

6. To be trained with known examples of a problem to acquire knowledge about it.
This trained network can be put to effective use in solving ‘unknown’ or ‘untrained’ instances of the problem.
Supervised Learning
A ‘teacher ‘ is assumed to be present during the learning process.
The network aims to minimize the error between the target output presented by the teacher and the computed output, to achieve better performance.

7. Unsupervised Learning
There is no teacher present to hand over the desired output and the network therefore tries to learn by itself organizing the input instances of the problem.
Neural Networks Architectures Classification
Single Layer Feed forward Networks
Multi Layer Feed forward Networks
Recurrent Networks

8. Stock Market Prediction
Neural Networks Application Areas
Pattern Recognition
Image Processing
Data Compression
Forecasting
Optimization
Stock Market Prediction

9. Neural Networks Systems
Backpropagation Network
Perceptron
ADALINE [Adaptive Linear Element]
Associative Memory
Boltzmann Machine
Adaptive Resonance Theory
Self-organizing feature map
Hopfield network

10. Fuzzy Logic
It try to capture the way humans represent and reason with
real world knowledge in the face of uncertainty.
Uncertainty could arise due to generality, vagueness, ambiguity,
chance or incomplete knowledge
The capability of fuzzy set to express gradual transitions from
membership to non-membership and vice versa has a broad
utility.

11. Operations on fuzzy sets
Union
Intersection
Subsethood
Composition of relations
Fuzzy Logic Multivalued truth values
True
Absolutely True
Fairly True
False
Absolutely False
Partly False

12. ● Fuzzy logic washing machines
These machines offer the advantages of performance, productivity, simplicity,
productivity, and less cost. Sensors continually monitor varying conditions
inside the machine and accordingly adjust operations for the best wash results.
Typically, fuzzy logic controls the washing process, water intake, water
temperature, wash time, rinse performance, and spin speed. This optimizes
the life span of the washing machine.
More sophisticated machines weigh the load , advise on the required amount
of detergent, assess cloth material type and water hardness, and check
whether the detergent is in powder or liquid form.
Some machines even learn from past experience, memorizing programs and
adjusting them to minimize running costs.

13. Genetic Algorithms
It initiated and developed in the early 1970 by John Holland are unorthodox search and optimization algorithms, which mimic some of the processes of natural evolution.
GAs perform random searches through a given set of alternatives 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 objective function which is usually referred to as a fitness function.

14. Genetic Operations
Reproduction Cross over Mutation Inversion
Dominance Deletion Duplication Translocation
Segregation Speciation Migration Sharing
Mating

15. things in order to create useful machines that can do work for humans.
Application: Robotics
Robotics involves human designers and engineers trying out all sorts of
things in order to create useful machines that can do work for humans.
Each robot's design is dependent on the job or jobs it is intended to do, so
there are many different designs out there.
GAs can be programmed to search for a range of optimal designs and
components for each specific use, or to return results for entirely new types
of robots that can perform multiple tasks and have more general application.
GA-designed robotics just might get us those nifty multi-purpose, learning
bots we've been expecting any year now .

16. Thanks…