2. Neural networks are simplified models of the
biological neuron system.
Neural network is a massively parallel
distributed processing system highly
interconnected neural computing elements.
Neural network refer to learning process in
trainning and to solve a problem using on
inference.
4. Neuron is a highly complex structure viewed
as a highly interconnected network of simple
processing elements.
Biological neuron is termed as artificial
neuron .
In this model basis of artificial neural
network.
6. x1,x2,x3 ……..xn are the n inputs.
w1,w2,…..,wn are the weights to be input
links.
The total input I received by the artificial neuron.
I=w1x1+w2x2+………..+wnxn.
n
wi xi
i=1
7. Artficial neural network is a define as a
dataprocessing system consisting a large
number of simple highly interconnected
processing elements in artificial neuron.
It can be represented using a directed graph.
A graph G is on order two tuples(V,E).
V represent set of vertices, E represent set of
edges.
8. The graph is directed call is a directed graph
or digraph.
v5
v3
v4v2
v1
e1
e2
e3
e4
e5
10. Single layer feedforward network have two
layers.
SLFFN
INPUT
LAYER
OUTPUT
LAYER
11. The input layer neurons receive the input
signal.
The output layer neurons receive the output
signal.
The input layer transmits the signals to the
output layer.
It is called as single layer feedforward
network.
13. Multi layer feedforward network has including
multiple layers.
MLFFN
INPUT
LAYER
OUTPUT
LAYER
HIDDEN
LAYER
14. Input layer and output layer have one or more
intermediary layers.
It is called ‘hidden layer’.
The computational units of the hidden layer
are know as the hidden neurons or hidden
units.
Input hidden layer weights:
The input layer neurons are linked to the
hidden layer neurons and the weights are link
on these are called ‘input hidden layer
weights’.
15. Input hidden layer weights:
The input layer neurons are linked to the
hidden layer neurons and the weights are link
on these are called “input hidden layer
weights”.
Hidden output layer weights:
Hidden layer neurons are linked to the output
layer neurons and weights are reffered to the
“hidden output layer weight”.
L-M1-M2-N.
17. Recurrent network is differ from feedforward
network architecture.
There is atleast one feedback loop.
There could also be neurons with self-
feedback links.
Example:
The output of a neuron is fed back into itself
as input.
19. The NNS exhibit mapping capabilities,that is,
they can map input patterns to their associated
output patterns.
The NNS posses the capability to generalize.thus
they can predict new outcomes from past trends.
The NNS are robust system and are fault
tolerant.they can,therefore recall full patterns
from incomplete,partial or noisy patterns.
The NNS can process information in parallel at
high speed and in a distributed manner.
