1.
IEEE Trans Syst Man Cybern B Cybern
; 34(1): 672-9, 2004 Feb.
Article
in English
| MEDLINE
| ID: mdl-15369106
ABSTRACT
This paper reports a cellular automata (CA) based model of associative memory. The model has been evolved around a special class of CA referred to as generalized multiple attractor cellular automata (GMACA). The GMACA based associative memory is designed to address the problem of pattern recognition. Its storage capacity is found to be better than that of Hopfield network. The GMACA are configured with nonlinear CA rules that are evolved through genetic algorithm (GA). Successive generations of GA select the rules at the edge of chaos. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.