1. NEURAL NETWORKS
Harshita Gupta (11508013)
Bioprocess Engineering

2.  What are ANN’s?
 Conventional computers VS ANNs
 How do they work?
 Why them?
 Applications

3. What Are Artificial Neural Network?
 An Artificial Neural Network (ANN) is an information
processing system
 It is composed of a large number of highly
interconnected processing elements (neurons)
working in unison to solve specific problems.
 ANNs, like people, learn by example, each configured
for a specific application
 Learning in biological systems involves adjustments to
the synaptic connections that exist between the
neurons. This is true of ANNs as well.
 Modern neural networks are non-linear statistical data
modeling tools. They are usually used to model
complex relationships between inputs and outputs or
to find patterns in data.

4. ANNs Vs Computers
 Neural networks  Conventional
process information in a computers use an
similar way the human algorithmic approach
brain does, composed
of a large number of
highly interconnected
processing units
 Neural networks learn  The problem solving
by example which must capability of
be selected very conventional computers
carefully. They cannot is restricted to
be programmed to problems that we
perform a specific task. already understand and
know how to solve.

5.  They cannot be  Conventional computers
programmed to perform a use a cognitive approach
specific task. The to problem solving; the
examples must be way the problem is to
selected carefully solved must be known and
otherwise useful time is stated in small
wasted or even worse the unambiguous instructions.
network might be  These machines are totally
functioning incorrectly. predictable; if anything
 The disadvantage is that goes wrong is due to a
because the network finds software or hardware fault.
out how to solve the
problem by itself, its
operation can be
unpredictable

6. A Human Neurons
As we are all familiar this is how a conventional neuron looks

7. Dendrites
Cell Body
Threshold
axon
Summation
A Network Neuron
In an artificial neural network, simple artificial nodes, variously called "neurons",
"neurodes", "processing elements" (PEs) or "units", are connected together to form a
network of nodes mimicking the biological neural networks — hence the term "artificial
neural network".

8. An Improved Neurod
W1
f(x)=K( iwigi(x))
W2
A more sophisticated neuron (figure
2) is the McCulloch and Pitts model
(MCP). Here inputs are 'weighted',
Wn the effect that each input has at
decision making is dependent on
the weight of the particular input. In
mathematical terms, the neuron
fires if and only if;
X1W1 + X2W2 + X3W3 + ... > T

9. Network Model
 The most common type of
artificial neural network
consists of three groups, or
layers, of units:
 a layer of "input" units is
connected to The activity of
the input units represents the
raw information that is fed
into the network.
 a layer of "hidden" units, The
activity of each hidden unit is
determined by the activities of
the input units and the
weights on the connections
between the input and the
hidden units.
 a layer of "output" units. The
behavior of the output units
depends on the activity of the
hidden units.

10. Network Architectures
Feed-forward networks Feedback networks
 Feed-forward ANNs  Feedback networks can
allow signals to travel have signals travelling in
one way only; from input
to output. The output of both directions by
any layer does not affect introducing loops in the
that same layer. network.
output
input
Hidden

11.  Feed-forward ANNs  Feedback networks
tend to be straight are dynamic; their
forward networks that 'state' is changing
associate inputs with continuously until they
outputs. reach an equilibrium
 This type of point. They remain at
organization is also the equilibrium point
input
output
referred to as bottom- until the input
up or top-down. changes and a new
equilibrium needs to
be found.
 Also known as
interactive or
recurrentHidden

12. Firing rules
 The firing rule is an important concept in
neural networks and accounts for their high
flexibility.
 A firing rule determines how one calculates
whether a neuron should fire for any input
pattern. It relates to all the input patterns, not
only the ones on which the node was trained.

13.  Take a collection of training patterns for a node,
some inputs in which
 Cause it to fire -1
 Prevent it from firing- 0
 Then the patterns not in the collection??
 Here the node fires when in comparison , they have
more input elements in common with the 'nearest'
pattern in the 1-taught set than with the 'nearest'
pattern in the 0-taught set.
 If there is a tie, then the pattern remains in the
undefined state.

14. For Example
 For example, a 3-input neuron is taught As
follows
 Output 1 when input (X1,X2 and X3) -111, 101
 Output 0 when the input is 000 or 001. ;

15. 111, 101=1 000 or 001=0
Before Generalization
X1 0 0 0 0 1 1 1 1
X2 0 0 1 1 0 0 1 1
X3 0 1 0 1 0 1 0 1
Out 0 0 0/1 0/1 0/1 1 0/1 1
After Generalization
X1 0 0 0 0 1 1 1 1
X2 0 0 1 1 0 0 1 1
X3 0 1 0 1 0 1 0 1
Out 0 0 0 0/1 0/1 1 1 1

16. Pattern Recognition
X11
X12 TAN
X13 1
F1
X21
X22 TAN
X23
2 F2
X31
X32 TAN F3
3
X33

17. X11 0 0 0 0 1 1 1 1
X12 0 0 1 1 0 0 1 1
Top Neuron
X13 0 1 0 1 0 1 0 1
Out 0 0 1 1 0 0 1 1
X21 0 0 0 0 1 1 1 1
X22 0 0 1 1 0 0 1 1 Middle Neuron
X23 0 1 0 1 0 1 0 1
Out 1 0/1 1 0/1 0/1 0 0/1 0
X31 0 0 0 0 1 1 1 1
X32 0 0 1 1 0 0 1 1
Bottom Neuro
X33 0 1 0 1 0 1 0 1
Out 0 0 1 1 0 0 1 0

19. Learning methods

20. Based on The memorization of
patterns response of the network
and the subsequent
 associative mapping in which the network learns to produce a particular
pattern on the set of input units whenever another particular pattern is
applied on the set of input units. The associative mapping can generally be
broken down into two mechanisms:
 auto-association: an input pattern is associated with itself and the states of input
and output units coincide. This is used to provide pattern competition, i.e. to
produce a pattern whenever a portion of it or a distorted pattern is presented. In
the second case, the network actually stores pairs of patterns building an
association between two sets of patterns.
 hetero-association: is related to two recall mechanisms:
 nearest-neighbor recall, where the output pattern produced corresponds to the input
pattern stored, which is closest to the pattern presented, and
 interpolative recall, where the output pattern is a similarity dependent interpolation of the
patterns stored corresponding to the pattern presented. Yet another paradigm, which is a
variant associative mapping is classification, i.e. when there is a fixed set of categories
into which the input patterns are to be classified.
 regularity detection in which units learn to respond to particular properties
of the input patterns. Whereas in associative mapping the network stores
the relationships among patterns, in regularity detection the response of
each unit has a particular 'meaning'. This type of learning mechanism is
essential for feature discovery and knowledge representation

21. Conclusion
The computing world has a lot to gain from neural networks. Their
ability to learn by example makes them very flexible and powerful.
Furthermore there is no need to devise an algorithm (or understand
inner mechanism) in order to perform a specific task; They are also
very well suited for real time systems because of their fast response
and computational times which are due to their parallel architecture.
Neural networks also contribute to other areas of research such as
neurology and psychology. They are regularly used to model parts of
living organisms and to investigate the internal mechanisms of the
brain.
Perhaps the most exciting aspect of neural networks is the
possibility that some day 'conscious' networks might be produced.
There is a number of scientists arguing that consciousness is a
'mechanical' property and that 'conscious' neural networks are a
realistic possibility.
Finally, I would like to state that even though neural networks have a
huge potential we will only get the best of them when they are

22. References
 An introduction to neural computing. Alexander, I. and Morton, H. 2nd edition
 http://media.wiley.com/product_data/excerpt/19/04713491/0471349119.pdf
 Neural Networks at Pacific Northwest National Laboratory
http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html
 Industrial Applications of Neural Networks (research reports Esprit, I.F.Croall,
J.P.Mason)
 A Novel Approach to Modeling and Diagnosing the Cardiovascular System
http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/keller.wcnn95.abs.html
 Artificial Neural Networks in Medicine
http://www.emsl.pnl.gov:2080/docs/cie/techbrief/NN.techbrief.html
 Neural Networks by Eric Davalo and Patrick Naim
 Learning internal representations by error propagation by Rumelhart, Hinton and
Williams (1986).
 Klimasauskas, CC. (1989). The 1989 Neuron Computing Bibliography.
Hammerstrom, D. (1986). A Connectionist/Neural Network Bibliography.
 DARPA Neural Network Study (October, 1987-February, 1989). MIT Lincoln Lab.
Neural Networks, Eric Davalo and Patrick Naim
 Assimov, I (1984, 1950), Robot, Ballatine, New York.
 Electronic Noses for Telemedicine
http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/keller.ccc95.abs.html
 Pattern Recognition of Pathology Images