1. Seminar on
UNDER THE GUIDANCE OF
Prof. K. E. Ch. Vidyasagar
PRESENTED BY
Aniket R. Jadhao
Dr. Bhausaheb Nandurkar College of Engineering&
Technology,
Yavatmal.
2012-2013

2. Contents
 Introduction
 Characteristics of ANN
 Biological neural networks
 Backpropogation Algorithm
 Advantages
 Applications
 Conclusion
 References

3. Introduction
 Neurocomputing is concerned with information
processing
 A neurocomputing approach to information
processing first involves a learning process within a
neural network architecture that adaptively
responds to inputs according to a learning rule

4. Cont...
 After the neural network has learned what it needs
to know , the trained network can be used to
perform certain tasks depending on a particular
application
 Neural networks have the capability to learn from
their environment and to adapt to it in an
interactive manner.

5. What do you think which is faster?
OR
A DIGITAL COMPUTER A HUMAN BEING?

6. Here come to answer ...
A human being is faster than Digital computer.
But why ?
How can we perform certain tasks better and faster
than a digital computer?
Do you know ?
Difference between brain and a digital computer?

7. Cont..
Neuron
Neurons are approximately six orders of
magnitude slower than silicon logic gates, However
the brain can compensate for the relatively slow
operational speed of the neuron by processing data in
a highly parallel architecture that is massively
interconnected. It is estimated that the human brain
must contain in the order of 10 raise to power 11
neurons and approximately three orders of magnitude
more connections or synapses
Therefore, the BRAIN is an
adaptive, nonlinear, parallel computer that is capable
of organizing neurons to perform certain tasks

8. Cont…
Figure: Neuron

9. Cont...
Characteristics of ARTIFICIAL NEURAL
NETWORKS
Ability to learn by example, An ARTIFICIAL NEURAL
NETWORK stores the knowledge that has been
learned during the training process in the synaptic
weights of neurons
 Ability to generalise

10. BACK-PROPAGATION ALGORITHIM
Figure 5: Backpropagation Training

11. Advantages of neurocomputing approach
to solving certain problems
Adaptive learning: An ability to learn how to do
tasks based on the data.
Self-Organization: An ANN can create its own
organization
Real Time Operation: ANN computations may be
carried out in parallel and special hardware devices
are being designed and manufactured which take
advantage of this capability
Fault Tolerance via Redundant Information Coding:
Partial destruction of a network leads to the
corresponding degradation of performance

12. Applications
 Voice Recognition - Transcribing spoken words into
ASCII text
 Target Recognition - Military application which uses
video and/or infrared image data to determine if an
enemy target is present
 Medical Diagnosis - Assisting doctors with their
diagnosis by analyzing the reported symptoms and/or
image data such as MRIs or X-rays
 Radar –signature classifier

13. Conclusion
Then the network is followed by the error generator
at the output, which compares the output of the
neuron with the target signal for which the network
has to be trained. Similarly, there is error generator
at the input, which updates the weights of the first
layer taking into account the error propagated back
from the output layer. Finally, a weight transfer unit is
present just to pass on the values of the updated
weights to the actual weights.

14. REFERNACES
[1] Simon Haykin, “Neural Networks”, Second edition
by, Prentice Hall of India, 2005.
[2] Christos Stergiou and Dimitrios Siganos, “Neural
Networks”, Computer Science Deptt. University of
U.K., Journal, Vol. 4, 1996.
[3] Robert J Schalkoff, “Artificial Neural Networks”,
McGraw-Hill International Editions, 1997.
[4] Uthayakumar Gevaran, “Back Propagation”,
Brandeis University, Department of Computer
Science.
[5] Jordan B.Pollack, “Connectionism: Past, Present
and Future”, Computer and Information Science
Department, The Ohio State University, 1998.

15. So, we can see that how neural networks and
neurocomputing is beneficial for us.
Thank you !
Instead of seeing society as a collection of clearly
defined “interest groups”, society must be
reconceptualise as a complex network of groups of
interacting individuals whose membership and
communication pattern are seldom confined to one
such group alone