Learning Internal Representation By Error Propagation.

implementing backpropagation by just trying to reconstruct.

Training of the networks is done with the back propagation method.

The ability to use diverse training algorithms (back propagation, contrastive divergence, etc.).

but you want to train it without having to do backpropagation.

Back Propagation (BP) Networks are the quintessential Neural Nets.

This forms a multi-layer perceptron which can then be fine tuned using backpropagation.

We disclose a method for adding recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency.

Back-propagation algorithms change the weights by propagating the error backwards from the output layer to the input layer while adjusting the weights.

Now, if cells can do that, they can for sure implement backpropagation and

Each ADM includes channels selectors (225, 255) for selecting at least one channel from each of the counterpropagating WDM signals.

For an incomplete data problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations.

The present invention provides a bidirectional WDM optical communication network including a bidirectional optical waveguide carrying counter-propagating WDM signals each including plural channels (20).

Thankfully, backpropagation provides a method for updating each weight between two neurons with respect to the output error.

Such feature spaces provide a good match to BPNN as a classifier because of the poor resolution of characters in video streams.