Learning Sequential Patterns by Nonmonotone Analog Neural Networks
Masahiko Morita
Abstract:
A learning method for nonmonotone analog neural networks is presented
by which almost any sequential pattern can be memorized.
This method does not require a complex learning rule or particular
devices for synchronizing neurons or delay;
one only has to change the input pattern gradually and modify
the synaptic weights according to a kind of correlation learning
rule.
Then the state of the network follows a little after the input
pattern, its trajectory growing into a dynamic attractor with a few
times of repetition.
Numerical simulations are performed to examine the learning
process and the recollection ability of the model.