The operation of Rosenblatt’s perceptron is based on the McCulloch and Pitts neuron model. The model consists of a linear combiner followed by a hard limiter. The weighted sum of the inputs is applied to the hard limiter, which
produces an output equal to þ1 if its input is positive and 1 if it is negative. The aim of the perceptron is to classify inputs, or in other words externally applied stimuli x1; x2; . . . ; xn, into one of two classes, say A1 and A2. Thus, in the case of
an elementary perceptron, the n-dimensional space is divided by a hyperplane into two decision regions. The hyperplane is defined by the linearly separable function