SOFT COMPUTING - PowerPoint PPT Presentation

• 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

• Expert systems
• Artificial neural networks
• Genetic Algorithm
• Fuzzy systems
• Swarm intelligence
• Ant Colony optimization
• Tabu Search method

Purpose of AI:

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

Expert systems:

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

user

Expansion facility

Knowledge update facility

User interface

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

GENETIC ALGORITHM

Search mechanism based on the Darwinian principle of natural evolution

• Chromosome
• Fitness function
• Initial population
• GA operators

Reproduction

Cross over

Mutation

• GA control parameters

• 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

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

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

• 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

• 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

• Adaptive learning
• Self-organization
• Real-time operation
• Fault tolerance via redundant information coding

FUZZY SYSTEMS

Technique to deal with imprecision and information granularity

• 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 making
• Multiperson decision making
• Multiobjective decision making
• Multiattribute decision making
• Fuzzy Bayesian decision making

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