Soft ComputingPresented by-

Manoj yadav

Abhishek tiwari

Harshal pandey

What is Soft Computing

Soft computing is a field of computer science which makes use of inexact solutions for problems which has no known method to compute an exact solution

It uses imprecision, uncertainty, partial truth, and approximation as input

GOALS OF SOFT OMPUTING

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

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.

SOFT COMPUTING -DEVELOPMENT HISTORY

Soft = Evolutionary + Neural + Fuzzy

Computing Computing Network Logic

Zadeh Rechenberg McCulloch Zadeh

1981 1960 1943 1965

Evolutionary = Genetic + Evolution + Evolutionary + Genetic

Computing Programming Strategies programming Algorithms

Rechenberg Koza Rechenberg Fogel Holland

1960 1992 1965 1962 1970

Hard Computing v/s Soft Computing

Hard computingDeals with precise valuesAccurate output is neededUseful in critical systems

Soft computing Deals with assumptionsAccuracy is not necessaryUseful for routine,control, decison making tasks

Techniques in Soft Computing

• Fuzzy Systems •Neural Networks •Evolutionary Computation • Machine Learning •Probabilistic Reasoning

Neural Networks

• Founded in 1940• Artificial neural network mimics the

biological neuron network in function

k

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 AssociationGeneralisationRobustnessFault ToleranceParallel and High speed information processing

Fuzzy Systems

• Based on fuzzy set theory and fuzzy logic• Uses numeric ranges of sets (fuzzy sets )

to measure and represent the logical evaluations of partially accurate findings

• Most applications in control and decision making

• Founded by Lofti A Zadeh

(Neuro)-fuzzy system construction

Training dataFuzzy rules(SOM, c-means etc.)

Experts

Control dataSystem

evaluation(errors)

Tuning(NN)

New system

FUZZY VERSES CRISP

FUZZYIS R AM HONEST ?

CRISP

FUZZY

ExtremelyHonest(1)

Very Honest(0.8)

Honest atTimes(0.4)

Dishonest(0Extremely

)

CRISP

YES!(1)

NO!(0)

IS WATER COLORLESS ?

OPERTIONS

CRISP FUZZY

1.Union 1.Union

2.Intersection 2.Intersection

3.Complement 3.Complement

4.Difference 4.Equality

5.Difference

6.Disjunctive Sum

Machine Learning

Pattern recognition based on training data,Classification supervised by instructor.

Unsupervised machine learning is also used where the machine learns from the given data by detecting patterns.

Orange

Apple?

Instructor

Advantages of SC

Models based on human reasoning. Closer to human thinking and biologically

inspired Models can be

Linguistic Comprehensible Fast when computing Effective in practice.

Soft Computing Applications

Heavy industry Robotic arms, Humanoid robots

Home appliances Washing machines, ACs,

Refrigerators, cameras

Automobiles Travel Speed Estimation, Sleep

Warning Systems, Driver-less cars

Spacecrafts Maneuvering of a Space

Shuttle(FL), Optimization of Fuel-efficient Solutions for space craft

SC applications: robotics

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.

Any Questions