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Identification of cellular automata based on incomplete observations with bounded time gaps
Abstract
In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed- journalArticle
- info:eu-repo/semantics/article
- info:eu-repo/semantics/publishedVersion
- Mathematics and Statistics
- Agriculture and Food Sciences
- LYAPUNOV EXPONENTS
- RULES
- Table lookup
- Genetic algorithms
- Automata
- Visualization
- Cybernetics
- Task analysis
- Machine learning algorithms
- Cellular automata (CAs)
- genetic algorithms (GAs)
- nonlinear dynamical systems
- system identification