Structure of two-layer feedforward neural network

A perceptron is a model of a biological neuron. The input to a perceptron is an M-dimensional vector, and each component/dimension of the vector is scaled by a weight. The sum of weighted inputs is computed and compared against a threshold. If the weighted sum exceeds the threshold, the output of the perceptron is '1'. Otherwise, the output of the perceptron is '-1' (or '0'). The output function of a perceptron is hard-limiting function. Thus the output of the perceptron is binary in nature. The following figure illustrates a perceptron.

where M = number of the elements in the input vector A two-layer feedforward neural network with hard-limiting output function for the unit in the output layer can be used to perform the task of pattern classification. The number of units in the input layer is equal to the dimension of the input vectors. The units in the input layer are all linear units, and the input layer merely contributes to fan-out the input to each of the the output units. The output layer may consist of one or more perceptrons. The number of perceptron units in the output layer depends on the number of distinct classes in the pattern classification task. If there are only two classes, then one perceptron in the output layer is sufficient. Two perceptrons in the output layer can be used when dealing with four different classes in the pattern classification task. Here, we consider a two-class classification problem, and hence only one perceptron in the output layer.