| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2022-09-15 |
| タイトル |
|
|
タイトル |
Entropy-based classification of elementary cellular automata under asynchronous updating : An experimental study |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
asynchronous cellular automata |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
classification |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
elementary cellular automata |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
robustness |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
uncertainty |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
entropy |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 著者 |
LEI, Qin
LEE, Jia
HUANG, Xin
KAWASAKI, Shuji
|
| 著者(機関) |
|
|
値 |
College of Computer Science, Chongqing University |
| 著者(機関) |
|
|
値 |
College of Computer Science, Chongqing University |
| 著者(機関) |
|
|
値 |
College of Computer Science, Chongqing University |
| 著者(機関) |
|
|
値 |
Faculty of Science and Engineering, Iwate University |
| 登録日 |
|
|
日付 |
2022-09-15 |
| 書誌情報 |
Entropy
巻 23,
号 2,
p. 209,
発行日 2021-02-08
|
| ISSN |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
10994300 |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Classification of asynchronous elementary cellular automata (AECAs) was explored in the first place by Fates et al. (Complex Systems, 2004) who employed the asymptotic density of cells as a key metric to measure their robustness to stochastic transitions. Unfortunately, the asymptotic density seems unable to distinguish the robustnesses of all AECAs. In this paper, we put forward a method that goes one step further via adopting a metric entropy (Martin, Complex Systems, 2000), with the aim of measuring the asymptotic mean entropy of local pattern distribution in the cell space of any AECA. Numerical experiments demonstrate that such an entropy-based measure can actually facilitate a complete classification of the robustnesses of all AECA models, even when all local patterns are restricted to length 1. To gain more insights into the complexity concerning the forward evolution of all AECAs, we consider another entropy defined in the form of Kolmogorov–Sinai entropy and conduct preliminary experiments on classifying their uncertainties measured in terms of the proposed entropy. The results reveal that AECAs with low uncertainty tend to converge remarkably faster than models with high uncertainty. |
| 抄録（URL） |
|
|
表示名 |
Entropy-Based Classification of Elementary Cellular Automata under Asynchronous Updating: An Experimental Study |
|
URL |
https://www.mdpi.com/1099-4300/23/2/209 |
| 出版者 |
|
|
出版者 |
MDPI |
| 権利 |
|
|
権利情報 |
© 2021 by the authors. |
| 権利URI |
|
|
権利情報 |
https://creativecommons.org/licenses/by/4.0/ |
| DOI |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
info:doi/10.3390/e23020209 |
| 著者版フラグ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
entropy-v23n2p209 (1.2 MB)
