Neural network apparatus and learning method thereof

Details Neural network apparatus and learning method thereof Neural network apparatus and learning method thereof

1997-06-11

1999-01-05

MacDonald, Allen R.

Data processing: artificial intelligence

Neural network

Structure

G06F 1518

Patent

active

058571787

ABSTRACT:
A neural network apparatus includes a neural network including at least two neuron layers each having a plurality of neurons and at least one synapse layer having a plurality of synapses each arranged between the neuron layers, each synapse storing a weight value between the neurons and multiplying the weight value with an output value from each of the neurons in the previous-stage neuron layer to output a product to the next-stage neuron layer, a section for causing an error signal between an output from the neural network and a desired output to back-propagate from an output-side neuron layer to an input-side neuron layer of the neural network, a learning control section for updating the weight value in the synapse on the basis of the error signal and the output value from the previous-stage neuron, and a selecting section for selecting a synapse whose weight value is to be updated by the learning control section when the learning control section is to update the weight values of a predetermined number of synapses in a predetermined order.

REFERENCES:
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Takeshi Shima, et al.; Neuro Chips with On-Chip Back-Propagation and/or Hebbian Learning; IEEE Journal of Solid-State Circuits, vol. 27, No. 12, pp. 1868-1876, Dec. 1992.
Jordan L. Holt, et al.; Finite Precision Error Analysis of Neural Network Electronic Hardware Implementations; Proceedings of International Joint Conference on Neural Networks, Seattle, 1, pp. 519-525, IEEE (1991).
Implementations; Proceding of International Joint Conference on Neural Networks, Seattle, 1, pp. 519-525, IEEE (1991).
Patrick A. Shoemaker, et al.; Back Propagation Learning with Trinary Quantization of Weight Updates; Neural Networks, vol. 4, pp. 231-241, 1991.
Christine Hubert, "Pattern Completion with the Random Neural Network using the RPROP Learning Algorithm", Systems, Man, and Cybernetics, 1993 Int. Conf., pp. 613-617, 1993.
G.E. Hinton and T.J. Sejnowski, "Learning and Relearning in Boltzmann Machines", in Parallel Distributed Processing, eds. David E. Rumelhart and James L. McClelland, pp. 282-317, 1986.

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