Generalized multiple attractor cellular automata (GMACA) model for associative memory

被引：14
|
作者：
Ganguly, N [1 ]
Maji, P [1 ]
Sikdar, BK [1 ]
Chaudhuri, PP [1 ]
机构：
[1] IISWBM, Ctr Comp, Kolkata 700073, W Bengal, India
来源：
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2002年 / 16卷 / 07期
关键词：
cellular automata; GA; edge of chaos; pattern recognition;
D O I：
10.1142/S0218001402001988
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper reports an efficient technique of evolving Cellular Automata (CA) as an associative memory model. The evolved CA termed as GMACA (Generalized Multiple Attractor Cellular Automata), acts as a powerful pattern recognizer. Detailed analysis of GMACA rules establishes the fact that the rule subspace of the pattern recognizing CA lies at the edge of chaos - believed to be capable of executing complex computation.
引用
收藏
页码：781 / 795
页数：15
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