Soft computing
This presentation is the property of its rightful owner.
Sponsored Links
1 / 20

SOFT COMPUTING PowerPoint PPT Presentation


  • 164 Views
  • Uploaded on
  • Presentation posted in: General

SOFT COMPUTING. DEFINITION:. 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 control Artificial Intelligence.

Download Presentation

SOFT COMPUTING

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Soft computing

SOFT COMPUTING


Definition

DEFINITION:

  • 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 control

  • Artificial Intelligence


Latest intelligent systems

Latest intelligent systems:

  • Expert systems

  • Artificial neural networks

  • Genetic Algorithm

  • Fuzzy systems

  • Swarm intelligence

  • Ant Colony optimization

  • Tabu Search method


Purpose of ai

Purpose of AI:

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


Expert systems

Expert systems:

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


Architecture of an expert system

Architecture of an expert system

user

Expansion facility

Knowledge update facility

User interface

Knowledge base

Inference engine


Characteristics of expert systems

Characteristics of expert systems:

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


Genetic algorithm

GENETIC ALGORITHM

Search mechanism based on the Darwinian principle of natural evolution


Components of ga

Components of GA

  • Chromosome

  • Fitness function

  • Initial population

  • GA operators

    Reproduction

    Cross over

    Mutation

  • GA control parameters


Characteristics of ga

Characteristics 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 differentiable

  • Implementation simpler – only information needed is objective function

  • Can solve non-linear , discontinuous optimal problems

  • perform well in noisy functions


Artificial neural networks

ARTIFICIAL NEURAL NETWORKS

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


Objective of ann

OBJECTIVE OF ANN

To develop a computational device for modeling the brain to perform various computational tasks at a faster rate than the traditional systems


Basic entities of ann

Basic entities of ANN:

  • the model’s synaptic interconnections

  • the training or learning rules adopted for updating and adjusting weights

  • their activation functions

    THE MAIN PROPERTY OF ANN IS ITS CAPABILITY TO LEARN


K inds of learning in ann

Kinds of learning in 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


Advantages of ann

Advantages of ANN:

  • Adaptive learning

  • Self-organization

  • Real-time operation

  • Fault tolerance via redundant information coding


Fuzzy systems

FUZZY SYSTEMS

Technique to deal with imprecision and information granularity


Fuzzification

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


Kinds of fuzzy decision making

Kinds of fuzzy decision making:

  • Individual decision making

  • Multiperson decision making

  • Multiobjective decision making

  • Multiattribute decision making

  • Fuzzy Bayesian decision making


Applications of fuzzy systems

Applications of Fuzzy systems:

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

  • traffic control

  • Steam engine

  • Aircraft flight control

  • Missile control

  • Adaptive control

  • Fault detection control unit

  • Power systems control


Thank you

THANK YOU


  • Login