2. Perceptron Network
• Weights between
input & output
units are
adjusted.
• Weights between
sensory
associator units
are fixed.
• Goal of
Perceptron net is
to classify the
input pattern as a
member on not
a member to a
particular class.
X1
Xi
1
Xn
Y
X0
X1
Xi
Xn
y
b
W1
W2
Wn
3. Adaline Network
• Receives input from
several units and one
unit called bias.
• Inputs are +1 or -1,
weights have sign +
or -
• Net input calculated
is applied to
quantizer function to
restore output to +1
or -1
• Compares actual
4. Madaline Network
• Contains “n” units of
input layer,”m” units of
adaline layers, “1” unit
of Madaline Layer.
• Each neuron in the
Adaline and madaline
layer have a bias of
excitation 1.
• Adaline layer is present
between input and
output Madaline Layer.
• Used in
Communication
Systems , equilizers and
noise cancellation
devices.
5. Back Propagation Network
• A multilayer Feed
forward network
consisting of Input,
hidden and output
layers.
• Hidden and output
layers have biases
whose activation is 1.
• Signals are reversed
in learning phase.
• Inputs sent to BPN
and outputs
obtained could be
6. Auto Associative Memory Network
• Training input and
target output vectors
are same.
• Input layers consist of n
input units & output
layer consist of n
output units.
• Input and output units
are connected
through weighted
interconnections.
• Input and output
vectors are perfectly
correlated with each
other component by
7. Maxnet
• Symmetrical weights
are present over the
weighted
interconnections.
• Weights between
neurons are inhibitory
and fixed.
• The maxnet with this
structure can be
used as a subnet to
select a particular
node whose net
input is the largest.
X1 Xm
Xi Xj
1 1
1
−𝜀
−𝜀
−𝜀
−𝜀
−𝜀
−𝜀
1
8. Mexican Hat Net
• Neurons are arranged
in a linear order such
that positive
connections exist
between Xi and
neighborhood units &
negative between Xi
and far away units.
• Positive region is
Cooperation and
negative region is
Competition.
• Size of these regions
depend on the
magnitude that exist
between positive and
X i X
i+1
X
i+2
X
i+3
X
i-1
X
i-2
X
i-3
W3
W3
W2 W2
W1 W1
𝛿𝑖
W0
Editor's Notes
Learning signal is the difference between the
desired and actual response of a neuron.
The perceptron learning rule is
Consider a finite “n” number of input training vectors
Associated target (desired) values x(n) and t(n) where n is
from 1 to N
Target is either +1 or -1
The output “y” is obtained on the basis of the net input
calculated and activation function being applied over the
net input
