SOFT COMPUTING

A field of study that encompasses computational techniques for performing tasks that require intelligence when performed by humans.

Simulation of human behavior and cognitive processes on a computer.

OTHER NAMES:Intelligent controlArtificial Intelligence

DEFINITION:

Expert systemsArtificial neural networksGenetic AlgorithmFuzzy systemsSwarm intelligenceAnt Colony optimizationTabu Search method

Latest intelligent systems:

To increase man’s understanding, reasoning, learning and perception for building new

developmental tools.

Purpose of AI:

Knowledge based program that provides expert quality solutions to problems in a specific domain

Expert systems:

Architecture of an expert system

user

User interfaceExpansion

facilityKnowledge

update facility

Knowledge base

Inference engine

Expertise Exhibit expert performance

Have high level of skill

Have adequate robustness

Symbolic reasoning Represent knowledge symbolically

Reformulate symbolic knowledge

Depth handle difficult problem domains

use complex rules

Self knowledge Examine its own operation

Characteristics of expert systems:

Search mechanism based on the Darwinian principle of natural evolution

GENETIC ALGORITHM

ChromosomeFitness functionInitial populationGA operators Reproduction Cross over

Mutation GA control parameters

Components of GA

Multi point search – reducing the probability of getting stuck in the local optima

Stochastic operators with guided search instead of deterministic rules

Objective function need not be differentiableImplementation simpler – only information

needed is objective functionCan solve non-linear , discontinuous optimal

problems perform well in noisy functions

Characteristics of GA

Information processing systems which are constructed and implemented to model the human

brain

ARTIFICIAL NEURAL NETWORKS

To develop a computational device for modeling the brain to perform various computational tasks at a

faster rate than the traditional systems

OBJECTIVE OF ANN

the model’s synaptic interconnections the training or learning rules adopted for

updating and adjusting weightstheir activation functions

THE MAIN PROPERTY OF ANN IS ITS CAPABILITY TO LEARN

Basic entities of ANN:

Supervised learning: The learning is performed with the help of teacher.

The correct target output values are known for each input pattern

Unsupervised learning:

self organizing in which exact clusters are formed by discovering similarities and dissimilarities among the objects

Reinforcement learning: learning with a critic as opposed to learning with a

teacher

Kinds of learning in ANN:

Adaptive learningSelf-organizationReal-time operationFault tolerance via redundant information

coding

Advantages of ANN:

Technique to deal with imprecision and information granularity

FUZZY SYSTEMS

Fuzzification:Process of transforming a crisp set to a fuzzy

set (fuzzy quantities)

Defuzzification: mathematically termed as “rounding it off”Mapping process from a space of fuzzy

control actions defines over an output universe of discourse into a space of crisp control actions

Individual decision makingMultiperson decision makingMultiobjective decision makingMultiattribute decision makingFuzzy Bayesian decision making

Kinds of fuzzy decision making:

Widely used in non-linear, time varying, ill-defined systems, complex systems like

traffic controlSteam engineAircraft flight controlMissile controlAdaptive controlFault detection control unitPower systems control

Applications of Fuzzy systems:

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