Polynomial cellular neural networks for implementing the game of life

被引：0

作者：

Pazienza, Giovanni Egidio ^{[1
]}

Gomez-Ramirez, Eduardo ^{[2
]}

Vilasis-Cardona, Xavier ^{[3
]}

机构：

[1] Univ Ramon Llull, GRSI, Quatre Camins 2, Barcelona 08022, Spain

[2] La Salle Univ, Posgrad Invest, LIDETEA, Mexico City 06140, DF, Mexico

[3] Univ Ramon Lull, Engn & Arquitect La Salle, LIFAELS, Barcelona 08022, Spain

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2007, PT 1, PROCEEDINGS | 2007年 / 4668卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One-layer space-invariant Cellular Neural Networks (CNNs) are widely appreciated for their simplicity and versatility; however, such structures are not able to solve non-linearly separable problems. In this paper we show that a polynomial CNN - that has with a direct VLSI implementation - is capable of dealing with the 'Game of Life', a Cellular Automaton with the same computational complexity as a Turing machine. Furthermore, we describe a simple design algorithm that allows to convert the rules of a Cellular Automaton into the weights of a polynomial CNN.

引用

页码：914 / +

页数：3

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