The Perceptron
Pattern Classification
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One of the purposes that neural networks are used for is pattern
classification.
Once the neural network has been trained using a learning
algorithm and a training set the neural network can classify an
input vector as either belonging to a particular class or category
or not.
Each training case is an input vector and target value pair.
 The input vector is composed of binary or bipolar values.
 The target value is a 1 if the pattern represented by the input
vector belongs to the class and a 0 or -1 if the input vector
does not belong to the class.
The perceptron learning algorithm is often used for purposes of
pattern classification.
The perceptron learning algorithm works better with bipolar
rather than binary values.
Perceptron
Perceptron
x1
x2
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1
w1
w2
y
wn
xn
Perceptron
b
Exercises
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Apply the perceptron algorithm to determine the
weights for the following functions, given theta is 0
and alpha is 1:
 AND
 OR
Train a perceptron neural network to store the
following patterns: (1 -1 1) and (1 1 -1) where the
first pattern belongs to class and the second does
not. Test the neural network on the following
patterns on the following noisy patterns: (0 -1 1), (0,
1, -1). Alpha is 1 and theta is 0.
Perceptron