Feed-forward Networks

Description:-The Single Layer Feed-forward Network consists of a single layer of weights, where the inputs are directly connected to the outputs, via a series of weights. The synaptic links carrying weights connect every input to every output , but not other way. This way it is considered a network of feed-forward type.

The sum of the products of the weights and the inputs is calculated in each neuron node, and if the value is above some threshold (typically 0) the neuron fires and takes the activated value (typically 1); otherwise it takes the deactivated value (typically -1).

Multi Layer Feed-forward Network:-The name suggests, it consists of multiple layers. The architecture of this class of network, besides having the input and the output layers, also have one or more intermediary layers called hidden layers. The

Computational units of the hidden layer are known as hidden neurons.

- The hidden layer does intermediate computation before directing theinput to output layer.

- The input layer neurons are linked to the hidden layer neurons; the weights on these links are referred to as input-hidden layer weights.

- The hidden layer neurons and the corresponding weights are referred to as output-hidden layer weights.

- A multi-layer feed-forward network with ℓ input neurons, m1 neurons in the first hidden layers, m2 neurons in the second hidden layers, and n output neurons in the output layers is written as (ℓ - m1 - m2 – n ).