Masahiko Morita, Shuji Yoshizawa and Kaoru Nakano
It has been considered that the autocorrelation type of associative
neural network does not work well unless stored patterns
are almost orthogonal with each other, which is an unsatisfactory
strong restriction on the representation of memory.
In this paper, it is reported that the autocorrelation model with
a nonmonotone function can memorize patterns which are substantially
Moreover, this model can make good use of the correlation and increase
its recollection ability.
It is also presented that using nonmonotone dynamics, correlation
learning can be improved in a natural manner so that it may have
the virtue of orthogonal learning.
Neural networks, Associative memory, Nonmonotone dynamics, Information
representation, Improvement of correlation learning.