Title :
Effects of noise in training patterns on the memory capacity of the fully connected binary Hopfield neural network: mean-field theory and simulations
Author_Institution :
Dept. of Comput. & Math., Deakin Univ., Clayton, Vic., Australia
fDate :
7/1/1998 12:00:00 AM
Abstract :
We show that the memory capacity of the fully connected binary Hopfield network is significantly reduced by a small amount of noise in training patterns. Our analytical results obtained with the mean field method are supported by extensive computer simulations
Keywords :
Hebbian learning; Hopfield neural nets; circuit noise; content-addressable storage; Hebbian learning; associative memory; binary Hopfield neural network; mean-field theory; memory capacity; noise effect; simulations; training patterns; CADCAM; Computational modeling; Computer aided manufacturing; Computer simulation; Hebbian theory; Hopfield neural networks; Intelligent networks; Neural networks; Neurons; Noise reduction;
Journal_Title :
Neural Networks, IEEE Transactions on
