1. Artificial Neural
Network
Coordinator:
Ms. Neha Chaudhary
Aditya
CS-3rdyear

2. Content
 Introduction
 History
 Capabilities
 Applications
 Real world implementation
 Issues
 Advantage/Disadvantage
 Conclusions
 References

3. Introduction
 Brain : A highly complex, non-linear &
parallel computing
 Structural constituent : ‘Neurons’
 A computer program designed to model
the human brain.

4. Biological Neuron
• Biological neuron

5. Conti…
 Our brains are made up of about 10
billion tiny units called neurons .
 Tree like Nerve fibres are called
dendrites .
 Signals coming into the neuron are
received via junctions called synapses .

6. Artificial Neuron
Dendrites
Synapses
The
information
transmission
happens at
Soma (cell body)
the synapses.
Axon

7. Conti…
 A network of interconnected functional
elements.
 A NN is trained to recognize and
generalize the relationship between a
set of inputs and outputs.

8. Artificial neural networks
Output
Inputs
Several inputs & one output.

9. History
 Inspiration for development came from
attempts to model the human central
nervous system.
 McCulloch-Pitts 1943 Introduced a
simple NN model using electrical
circuits.

10. Conti…
 Hebb 1949 Wrote that neural pathways
are strengthened every time they are
used.
 Minsky 1954: Learning Machine.
 Rosenblatt’s Perceptron 1957.

11. Conti…
 Widrow 1960 : Adaline model.
 Recent work includes Boltzmann
machines, competitive learning models,
multilayer networks, and adaptive
resonance theory models.

12. Artificial neurons
 Neurons work by processing
information.
x1
x2 w1
n Output
x3 w2 z = ∑ wi xi ; y = H ( z )
i =1 y
w3
… ..
. w
n-1
xn-1
wn
xn
The McCulloch-Pitts model

13. Capabilities
 Non-linearity
 Input/Output mapping
 Adaptivity
 Evidential response
 Fault tolerence
 VLSI implementability

14. Applications
Robotics
Image processing
 Speech/Pattern recogntion
Gaming
 Target Recognition
 Medical Diagnosis
 Voice and touch interface with
computers and other devices.

15. Real World Implementation
 Logical reasoning
 Pattern recognition
 Planning
 Genetic programming
 Common sense knowledge
 Representation
 Control system

16. Issues
 Complex programs
 Difficult to implement
 Machine prediction may not be accurate
 Human beings may lose their importance

17. Advantage
 Pattern recognition
 Does not need to be reprogrammed
 Implemented in any application
 Adaptive learning
 Self-Organisation
 Real Time Operation
 Fault Tolerance

18. Disadvantage
 Loss of human control.
 They may dominant us in the near
future.

19. Conclusions
 Once NNs are trained they can be
reapplied over and over again.
 Can be linked with other models to solve
complex problems.

20. Conti…
 Neural Networks LEARN. They are not
programmed.
 Can be applied to areas where humans
are often wrong too.

21. References
 www.wikipedia.org
 www.ai-junkie.com
 “Artificial Neural Network” by
B.Yegnanarayana

22. Thank You