ANN details with examples perceptron types

Neuron Model
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McCulloch Pitts Model Rosenblatt
perceptron model
Adaptive Linear element (ADALINE)
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McCulloch Pitts Model
● In McCulloch-Pitts (MP) model the activation (x) is given
by a weighted sum of its M input values (ai) and a bias term.
The output signal (s) is typically a nonlinear function f(x) of the
activation value x.
Networks consisting of MP neurons with binary (on-off) output
signals can be configured to perform several logical functions.
In the MP model the weights are fixed. Hence a network using
this model does not have the capability of learning.
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McCulloch Pitts Model
The following equations describe the operation of an MP model:

Rosenblatt Perceptron Model
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It was designed by Rosenblatt in 1958 to overcome most of the issues of the
McCulloch Pitts model.
1. It can process non boolean inputs
2. It can assign different weights to each input.
3. The threshold is computed automatically.
The Rosenblatt's perceptron model for an artificial neuron consists of outputs
from sensory units to a fixed set of association units the outputs of which are
fed to an MP neuron.
The main deviation from the M P model is that learning(i.e., adjustment of
weights) is incorporated in the operation of the unit. The desired or target
output (b) is compared with the actual binary output (s), and the error (e) is
used to adjust the weights.
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Adaptive Linear element
(ADALINE)
Algorithm:
Weight and bias are set to some random values
but not zero.
Calculate the net input to output unit untill the
least mean sqared error is obtained.
Error= target value – activated value update the
weight and bias for i=1 to n.
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Adaptive Linear element
(ADALINE)

ANN details with examples perceptron types

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