Vector Associative Memory Models.

Kryzhanovskii, B. V.; Litinskii, L. B.

The Hopfield model effectively stores a comparatively small number of initial patterns, about 15% of the size of the neural network. A greater value can be attained only in the Potts-glass associative memory model, in which neurons may exist in more than two states. Still greater memory capacity is exhibited by a parametric neural network based on the nonlinear optical signal transfer and processing principles. A formalism describing both the Potts-glass associative memory and the parametric neural network within a unified framework is developed. The memory capacity is evaluated by the Chebyshev–Chernov statistical method.

ARTIFICIAL neural networks; NEURAL computers; COMPUTER storage devices; COMPUTER science; COMPUTER industry

Automation & Remote Control, 2003, Vol 64, Issue 11, p1782

0005-1179

Academic Journal

10.1023/A:1027386531462