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异步调谐自动机的计算能力增强混沌的展开边缘。

Computational Power of Asynchronously Tuned Automata Enhancing the Unfolded Edge of Chaos.

作者信息

Gunji Yukio-Pegio, Uragami Daisuke

机构信息

Department of Intermedia, Art and Science, School of Fundamental Science and Technology, Waseda University, 3-4-1, Ohkubo, Shinjuku, Tokyo 169-8555, Japan.

Department of Mathematical Engineering, College of Industrial Technology, Nihon University, 1-2-1, Izumi-cho, Narashino 275-8575, Japan.

出版信息

Entropy (Basel). 2021 Oct 20;23(11):1376. doi: 10.3390/e23111376.

DOI:10.3390/e23111376
PMID:34828074
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8622964/
Abstract

Asynchronously tuned elementary cellular automata (AT-ECA) are described with respect to the relationship between active and passive updating, and that spells out the relationship between synchronous and asynchronous updating. Mutual tuning between synchronous and asynchronous updating can be interpreted as the model for dissipative structure, and that can reveal the critical property in the phase transition from order to chaos. Since asynchronous tuning easily makes behavior at the edge of chaos, the property of AT-ECA is called the unfolded edge of chaos. The computational power of AT-ECA is evaluated by the quantitative measure of computational universality and efficiency. It shows that the computational efficiency of AT-ECA is much higher than that of synchronous ECA and asynchronous ECA.

摘要

针对主动更新与被动更新之间的关系描述了异步调谐元胞自动机(AT - ECA),这阐明了同步更新与异步更新之间的关系。同步更新与异步更新之间的相互调谐可被解释为耗散结构模型,这能够揭示从有序到混沌的相变中的关键特性。由于异步调谐容易使行为处于混沌边缘,AT - ECA的这一特性被称为混沌的展开边缘。通过计算通用性和效率的定量度量来评估AT - ECA的计算能力。结果表明,AT - ECA的计算效率远高于同步元胞自动机(ECA)和异步ECA。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/3ecc815291ce/entropy-23-01376-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/c836651862ab/entropy-23-01376-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/dcae769d2b84/entropy-23-01376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/e4c236b81014/entropy-23-01376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/3e87174cefd7/entropy-23-01376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/64df69490480/entropy-23-01376-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/8a196b387677/entropy-23-01376-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/3ecc815291ce/entropy-23-01376-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/c836651862ab/entropy-23-01376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/69f024aaf4fe/entropy-23-01376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/dcae769d2b84/entropy-23-01376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/e4c236b81014/entropy-23-01376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/3e87174cefd7/entropy-23-01376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/64df69490480/entropy-23-01376-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/8a196b387677/entropy-23-01376-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fa1/8622964/3ecc815291ce/entropy-23-01376-g008.jpg

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