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McCulloch-Pitts neuron model was enhanced by Frank Rosenblatt in 1957 where he proposed the concept of the perceptron [2] to solve linear classification problems. This algorithm supervises the learning process of binary classifiers. This binary single neuron model merges the concept of McCulloch-Pitts model [1] with Hebbian learning rule of adjusting weights [3]. In perceptron, an extra constant, termed as bias, is added. The decision boundary can be shifted by bias away from the origin. It is independent of any input value. To define perceptron, Equation (2.1) has been modified as follows:

(2.3)

where

b ≡ bias value.