Two-dimensional cellular automaton evolution rule classification method

Abstract

The invention discloses a two-dimensional cellular automaton evolution rule classification method and belongs to the technical field of a cellular automaton. According to the method, under the random initial conditions, statistics of change conditions of cellular states of each position in the certain-scale evolution space is carried out, and classification is carried out; if within plenty of continuous time steps, the evolution space at each time and the stable evolution space have non-big difference, and the evolution space is a stationary type; if within plenty of continuous time steps, one period exists, the evolution space at each time in the period and the evolution space at the time of the next period have non-big difference, the evolution space is a period type; if within plenty of continuous time steps, the evolution space at each time is complex, the evolution space at one time has a relatively large quantity of cellulars in an update state compared with the evolution space at the previous time, update is irregular, the evolution space is a complex type; otherwise, the evolution space is a confused type. The method is advantaged in that the method can be applied to image encryption and image compression, application evolution rules are wide, and accurate classification can be realized.

Description

technical field [0001] The invention belongs to the technical field of cellular automaton (Cellular Automaton), and specifically relates to a classification process based on evolution rules of two-dimensional cellular automaton. Background technique [0002] A cellular automaton is a dynamical system that is discrete in time and space. Each cell (Cell) scattered in the evolution rule grid (LatticeGrid) takes a finite discrete state, follows the same action evolution rule, and performs synchronous update according to the determined local evolution rule. A large number of cells constitute the evolution of the fine state system through simple interactions. [0003] People classify the evolution rules of cellular automata from different angles, such as dynamics, linguistics, topology, and dimensions. Wolfram's qualitative classification of evolution rules for one-dimensional cellular automata in terms of dynamical behavior is by far the most influential. Researchers have done...

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Problems solved by technology

If in the evolution space of cellular automata, the proportion of living cells in one part of the space has decreased a lot, while the proportion of living cells in another part of the space has increased a lot, if the proportion of decline is similar to the proportion of rise, resulting in The proportion of living cells in the entire evolution space remains stable, but in fact the evolution rule of this kind of cellular automata is not stable

[0007] The existing classification methods classify the evolution rules of additive two-dimensional cellular automata, but cannot complete the classification processing of non-additive evolution rules, reducing a large number of evolution rules

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