2.  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

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

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

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

6. user
Knowledge Expansion
User interface facility
update facility
Knowledge Inference
base engine

7. 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

8. Search mechanism based on the Darwinian principle of
natural evolution

9.  Chromosome
 Fitness function
 Initial population
 GA operators
Reproduction
Cross over
Mutation
 GA control parameters

10.  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

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

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

13.  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

14.  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

15.  Adaptive learning
 Self-organization
 Real-time operation
 Fault tolerance via redundant information coding

16. Technique to deal with imprecision and information
granularity

17.  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

18.  Individual decision making
 Multiperson decision making
 Multiobjective decision making
 Multiattribute decision making
 Fuzzy Bayesian decision making

19. 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