Patent
1991-06-27
1992-04-07
Fleming, Michael R.
G06F 1518, G06F 1546
Patent
active
051034964
ABSTRACT:
An artificial neural network, which has a plurality of neurons each receiving a plurality of inputs whose effect is determined by adjust able weights at synapses individually connecting the inputs to the neuron to provide a sum signal to a sigmoidal function generator determining the output of the neuron, undergoes memory modification by a steepest-descent method in which individual variations in the outputs of the neurons are successively generated by small perturbations imposed on the sum signals. As each variation is generated on the output of a neuron, an overall error of all the neuron outputs in relation to their desired values is measured and compared to this error prior to the perturbation. The difference in these errors, with adjustments which may be changed as the neuron outputs converge toward their desired values, is used to modify each weight of the neuron presently subjected to the perturbation.
REFERENCES:
patent: 4660166 (1987-04-01), Hopfield
patent: 4719591 (1988-01-01), Hopfield et al.
Widrow, B. and Hoff, M. E.; "Adaptive Switching Circuits", Institute of Ro Engineers, Western Electric Show and Convention, convention Record, Part 4, pp. 96-104 (1960).
Widrow, B. and Winter, R. G., and Baxter, R. A.; "Learning Phenomena in Layered Networks", IEEE First International Conference on Neural Networks, (Sep. 1988).
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.; "Learning Internal Representations by Error Propagation", Parallel Distributed Processing, Chap. 8, pp. 318-328, The MIT Press, Cambridge, Massachusetts (1986).
IEEE First International Conference on Neural Networks, San Diego, California, Jun. 21-24, 1987, IEEE, A. G. Barto et al.: "Gradient following without back-propagation in layered networks", pp. II/629-636; see p. 629, line 1 to p. 631, line 20.
Journal of Electronic Engineering, vol. 24, No. 256, Apr. 1988 (Tokyo, JP), "Disturbance generator improves recognition in neuron computers", pp. 74-77; see p. 75, left-hand col., line 33 to right-hand col., line 8.
IEEE ASSP Magazine, Apr. 1987, IEEE, R. P. Lippman: "An introduction to computing with neural nets", pp. 4-22.
Andes David K.
Barbieri James F.
Licklider Robert A.
Swenson Richard M.
Witcher Donald H.
Church Stephen J.
Davis George
Fleming Michael R.
Forrest, Jr. John L.
Sliwka Melvin J.
LandOfFree
Artificial neural network system for memory modification does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Artificial neural network system for memory modification, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Artificial neural network system for memory modification will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-1901647