Recurrent neural network with variable size intermediate layer

Details Recurrent neural network with variable size intermediate layer Recurrent neural network with variable size intermediate layer

1991-07-10

1992-07-07

MacDonald, Allen R.

G06F 1518

Patent

active

051290391

ABSTRACT:
The present invention is concerned with a signal processing system having a learning function pursuant to the back-propagation learning rule by the neural network, in which the learning rate is dynamically changed as a function of input values to effect high-speed stable learning. The signal processing system of the present invention is so arranged that, by executing signal processing for the input signals by the recurrent network formed by units each corresponding to a neuron, the features of the sequential time series pattern such as voice signals fluctuating on the time axis can be extracted through learning the coupling state of the recurrent network. The present invention is also concerned with a learning processing system adapted to cause the signal processing section formed by a neural network to undergo signal processing pursuant to the back-propagation learning rule, wherein the local minimum state in the course of the learning processing may be avoided by learning the coefficient of coupling strength while simultaneously increasing the number of the unit of the intermediate layer.

REFERENCES:
Neural Net Pruning--Why and How; Sietsma et al.; Proceedings of IEEE Int. Conf. on Neural Networks; vol. I; pp. 325-333; 1988.

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