backpropagation neural network

Back-propagation networks
This algorithm is based on the error-correction learning rule. Basically, the error back-propagation process consists of two passes through the different layers of the network: a forward pass and a backward pass.
In the forward pass, activity pattern (input vector) is applied to the sensory nodes of the network, and its effect propagates through the network, layer by layer. Finally, a set of outputs is produced as the actual response of the network. During the forward pass the synaptic weights of network are all fixed.
During the backward pass, on the other hand,
• The synaptic weights are all adjusted in accordance with the error-correction rule.
• Specifically, the actual response of the network is subtracted from a desired (target) response to produce an error signal.
• This error signal is then propagated backward through the network, against direction of synaptic connections -hence the name “error back-propagation”.
• The synaptic weights are adjusted so as to make the actual response of the network move closer the desired response.