1. SOFT COMPUTING

2. What is Soft Computing?
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 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.

4. SOFT COMPUTING - DEVELOPMENT HISTORY
Soft = Evolutionary + Neural + Fuzzy Computing Computing Network Logic Zadeh Rechenberg McCulloch Zadeh Evolutionary = Genetic + Evolution + Evolutionary + Genetic Computing Programming Strategies programming Algorithms Rechenberg Koza Rechenberg Fogel Holland

5. 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
Generalisation
Robustness
Fault Tolerance
Parallel and High speed information processing

6. Neuron
Biological neuron
Model of a neuron

7. ANN ARCHITECTURES Xi - Input Neuron Yi - Hidden /Output Neuron
Input Layer Output Layer
1.Single Layer Feedforward Network
Input Layer Hidden Layer Output Layer
2.Multilayer Feedforward Network
Input Layer Hidden Layer Output Layer
3.Recurrent Networks
Xi - Input Neuron
Yi - Hidden /Output Neuron
Zi - Output Neuron
i = 1,2,3,4….. X1 y1 X1 z1 y1 X2 y2 X2 z2 y2 X3 y3 X3 z3 X1 z1 y1 X2 z2 y2 z3 X3

8. LEARNING METHODS OF ANN
NN Learning algorithms
SSupervised
Learning
Unsupervised
Learning
Reinforced
Learning
Error
Correction
Stochastic
Hebbian
Competitive
Least Mean
Square
Backpropagation

9. 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

10. FUZZY VERSES CRISP FUZZY CRISP IS R AM HONEST ? IS WATER COLORLESS ?
Extremely
Honest(1)
YES!(1)
Very
Honest(0.8)
FUZZY
CRISP
Honest at
Times(0.4)
NO!(0)
Extremely
Dishonest(0)

11. OPERTIONS 1.Union 2.Intersection 3.Complement 4.Difference
CRISP
FUZZY
1.Union 2.Intersection 3.Complement 4.Difference
1.Union 2.Intersection 3.Complement 4.Equality 5.Difference 6.Disjunctive Sum

12. PROPERTIES FUZZY CRISP Commutativity Commutativity Associativity
Distributivity
Idempotence
Identity
Law Of Absorption
Transitivity
Involution
De Morgan’s Law
Commutativity
Associativity
Distributivity
Idempotence
Identity
Law Of Absorption
Transitivity
Involution
De Morgan’s Law
Law Of the Excluded Middle
Law Of Contradiction

13. 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 tp 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.

14. BIOLOGICAL BACKGROUND
All living organism consist of cell. In each cell, there is a set of chromosomes which are strings of DNA and serves as a model of the organism. A chromosomes consist of genes of blocks of DNA. Each gene encodes a particular pattern. Basically, it can be said that each gene encodes a traits. Fig. Genome consisting Of chromosomes. A G A G C T A A G C T T C

15. ENCODING There are many ways of representing individual genes.
Binary Encoding
Octal Encoding
Hexadecimal Encoding
Permutation Encoding
Value Encoding
Tree Encoding

16. BENEFITS OF GENETIC ALGORITHM
Easy to understand.
We always get an answer and the answer gets better with time.
Good for noisy environment.
Flexible in forming building blocks for hybrid application.
Has substantial history and range of use.
Supports multi-objective optimization.
Modular, separate from application.

17. APPLICATION OF SOFT COMPUTING
Consumer appliance like AC, Refrigerators, Heaters, Washing machine.
Robotics like Emotional Pet robots.
Food preparation appliances like Rice cookers and Microwave.
Game playing like Poker, checker etc.

18. FUTURE SCOPE
Soft Computing can be extended to include bio- informatics aspects.
Fuzzy system can be applied to the construction of more advanced intelligent industrial systems.
Soft computing is very effective when it’s applied to real world problems that are not able to solved by traditional hard computing.
Soft computing enables industrial to be innovative due to the characteristics of soft computing: tractability, low cost and high machine intelligent quotient.

19. Thanks..