Soft Computing

Introduction

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.

• 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.

• Hybrid Systems

Neural Networks Genetic

Algorithms

Fuzzy Logic

x

y

zk

x Neuro Fuzzy

y Neuro Genetic Algorithms

z Genetic Algorithms Fuzzy

k Neuro Fuzzy Genetic Algorithms

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

• 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.

• 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

Neural Networks Application Areas

• Pattern Recognition• Image Processing• Data Compression• Forecasting• Optimization • Stock Market Prediction

Neural Networks Systems

• Backpropagation Network• Perceptron• ADALINE [Adaptive Linear Element]• Associative Memory• Boltzmann Machine • Adaptive Resonance Theory• Self-organizing feature map• Hopfield network

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.

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

● 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 optimises

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,memorising programs and

adjusting them to minimise running costs.

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.

Genetic Operations

Reproduction Cross over Mutation Inversion

Dominance Deletion DuplicationTranslocation

Segregation Speciation Migration Sharing

Mating

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 .

• References

Neural Networks, Fuzzy Logic & Genetic Algorithms –Synthesis &

applications, T.S. Rajasekaran & G.A. Vijaylakshmi Pai, PHI

http://cs.stanford.edu/people/eroberts/courses/soco/projects/2000-

01/neural-networks/index.html

http://www.samsung.com/in/consumer/home-appliances/washing-m

achines/front-loading/WF700B0BKWQ/TL

http://brainz.org/15-real-world-applications-genetic-algorithms/