Hidden Layer Computation

The p^th neuron of the hidden layer is shown below. It has input from the output of the input neurons layers. If we consider transfer function as sigmoidal function then the output of the p^th hidden neuron is given by

Where O_Hp is the output of the p^th hidden neuron,I_Hp is the input of the p^th hidden neuron, and θ_HP is the threshold of the p^th neuron;

A non-zero threshold neuron, is computationally equivalent to an input that is always held at -1 and the non-zero threshold becomes the connecting weight value.

Treating each component of the input of the hidden neuron separately, we get the outputs of the hidden neuron as given by above equation . The input to the output neuron is the weighted sum of the outputs of the hidden neurons. Accordingly, Ioq the input to the qth output neuron is given by the equation

It denotes weight matrix or connectivity matrix between hidden neurons and output neurons as [ W ], we can get input to output neuron as