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

页码：781 / 795

页数：15

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