元胞自动机的控制与观察问题

IF 0.7 4区 数学 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
S. Yacoubi, T. Plénet, S. Dridi, F. Bagnoli, L. Lefévre, C. Raïevsky
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引用次数: 1

摘要

本文主要研究控制理论背景下元胞自动机的可观察性和可控性问题,元胞自动机的一个重要特征是采用状态空间模型。我们的工作首先包括将得到的结果推广到由ca描述的系统,这些系统被认为是偏微分方程的离散对应,并探索证明可控性和可观察性的可能方法。在元胞自动机模型中引入控制和观察的概念之后,以类似于离散时间分布参数系统的方式,我们在复杂系统的情况下研究控制理论的这些关键概念。对于可控性问题,布尔类是专门研究和应用于区域情况的,而可观察性问题是在一般情况下探讨的,与线性或非线性ca的可重构性问题有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some Control and Observation Issues in Cellular Automata
This review article focuses on studying problems of observability and controllability of cellular automata (CAs) considered in the context of control theory, an important feature of which is the adoption of a state-space model. Our work first consists in generalizing the obtained results to systems described by CAs considered as the discrete counterpart of partial differential equations, and in exploring possible approaches to prove controllability and observability. After having introduced the notion of control and observation in cellular automata models, in a similar way to the case of discrete-time distributed parameter systems, we investigate these key concepts of control theory in the case of complex systems. For the controllability issue, the Boolean class is particularly studied and applied to the regional case, while the observability is approached in the general case and related to the reconstructibility problem for linear or nonlinear CAs.
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来源期刊
Advances in Complex Systems
Advances in Complex Systems 综合性期刊-数学跨学科应用
CiteScore
1.40
自引率
0.00%
发文量
121
审稿时长
6-12 weeks
期刊介绍: Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.
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