Design and characterization of cellular automata based associative memory for pattern recognition

被引:29
|
作者
Ganguly, N [1 ]
Maji, P
Sikdar, BK
Chaudhuri, PP
机构
[1] IISWBM, Ctr Comp, Kolkata 700073, W Bengal, India
[2] Bengal Engn Ctr, Dept Comp Sci & Technol, Kolkata 711103, W Bengal, India
关键词
associative memory; CA; GA; GMACA;
D O I
10.1109/TSMCB.2002.806494
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 [1], [2]. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.
引用
收藏
页码:672 / 679
页数:8
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