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In addition to the $lambda$ parameter, we have found another parameter which characterize the class III, class II and class IV patterns more quantitatively. It explains why the different classes of patterns coexist at the same $lambda$. With this parameter, the phase diagram for an one dimensional cellular automata is obtained. Our result explains why the edge of chaos(class IV) is scattered rather wide range in $lambda$ around 0.5, and presents an effective way to control the pattern classes. oindent PACS: 89.75.-k Complex Systems
It is shown that for the N-neighbor and K-state cellular automata, the class II, class III and class IV patterns coexist at least in the range $frac{1}{K} le lambda le 1-frac{1}{K} $. The mechanism which determines the difference between the pattern
The mechanism which discriminates the pattern classes at the same $lambda$, is found. It is closely related to the structure of the rule table and expressed by the numbers of the rules which break the strings of the quiescent states. It is shown that
Cellular Automaton (CA) and an Integral Value Transformation (IVT) are two well established mathematical models which evolve in discrete time steps. Theoretically, studies on CA suggest that CA is capable of producing a great variety of evolution pat
This paper proposes several algorithms and their Cellular Automata Machine (CAM) for drawing the State Transition Diagram (STD) of an arbitrary Cellular Automata (CA) Rule (any neighborhood, uniform/ hybrid and null/ periodic boundary) and length of
In this paper, we explore the two-dimensional behavior of cellular automata with shuffle updates. As a test case, we consider the evacuation of a square room by pedestrians modeled by a cellular automaton model with a static floor field. Shuffle upda