OPTIMAL CNN TEMPLATES FOR LINEARLY-SEPARABLE ONE-DIMENSIONAL CELLULAR AUTOMATA-Reference-Cited by-同舟云学术

OPTIMAL CNN TEMPLATES FOR LINEARLY-SEPARABLE ONE-DIMENSIONAL CELLULAR AUTOMATA

Published:2007-03 Issue:03 Volume:17 Page:749-790
ISSN:0218-1274
Container-title:International Journal of Bifurcation and Chaos
language:en
Short-container-title:Int. J. Bifurcation Chaos

Author:

CHANG P. J.¹,MUTHUSWAMY BHARATHWAJ¹

Affiliation:

1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA

Abstract

In this tutorial, we present optimal Cellular Nonlinear Network (CNN) templates for implementing linearly-separable one-dimensional (1-D) Cellular Automata (CA). From the gallery of CNN templates presented in this paper, one can calculate any of the 256 1-D CA Rules studied by Wolfram using a CNN Universal Machine chip that is several orders of magnitude faster than conventional programming on a digital computer.

Publisher

World Scientific Pub Co Pte Lt

Subject

Applied Mathematics,Modeling and Simulation,Engineering (miscellaneous)

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218127407017884

Reference5 articles.

1. Cellular Neural Networks and Visual Computing

2. A NONLINEAR DYNAMICS PERSPECTIVE OF WOLFRAM'S NEW KIND OF SCIENCE PART I: THRESHOLD OF COMPLEXITY

3. A Nonlinear Dynamics Perspective of Wolfram's New Kind of Science Part II: Universal Neuron

4. CNN Genes for One-Dimensional Cellular Automata: A Multi-Nested Piecewise-Linear Approach

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