Title :
Hopfield learning rule with high capacity storage of time-correlated patterns
Author :
Storkey, Amos J. ; Valabregue, R.
Author_Institution :
Neutral Syst. Group, Imperial Coll. of Sci., Technol. & Med., London, UK
fDate :
10/9/1997 12:00:00 AM
Abstract :
A new local and incremental learning rule is examined for its ability to store patterns from a time series in an attractor neural network. This learning rule has a higher capacity than the Hebb rule, and suffers significantly less capacity loss as the correlation between patterns increases
Keywords :
Hopfield neural nets; content-addressable storage; learning (artificial intelligence); time series; Hopfield learning rule; attractor neural network; high capacity storage; local and incremental learning rule; time series; time-correlated patterns;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19971233
