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Recurrent neural networks teaching system










Image Number 7 for United States Patent #5182794.

A teaching method for a recurrent neural network having hidden, output and input neurons calculates weighting errors over a limited number of propagations of the network. This process permits the use of conventional teaching sets, such as are used with feedforward networks, to be used with recurrent networks. The teaching outputs are substituted for the computed activations of the output neurons in the forward propagation and error correction stages. Back propagated error from the last propagation is assumed to be zero for the hidden neurons. A method of reducing drift of the network with respect to a modeled process is also described and a forced cycling method to eliminate the time lag between network input and output.








 
 
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