Network Analysis Of Cellular Automata Based On Complex Network

Cellular automata(CA)is a dynamic system which is discrete in time and space.Its evolutionary rules are simple and local,but its global dynamic evolution behavior is complex and chaotic.There are evolutionary rules for the growth,change and development of everything in the world,and these laws are just like the evolution laws of cellular automata.Therefore,studying the regular space and evolution track of cellular automata will help us better apply it to production practices and promote the understanding of the mechanism behind some social phenomena.At present,we can effectively analyze and characterize the evolution law of cellular automata from the known point of view,but because of the variety of cellular automata,it is difficult to systematically get the universal conclusion of its evolution law.Therefore,it is still a challenge about the global dynamic analysis of cellular automata.Based on the research of Kayama et al,this thesis further studies the dynamic properties of the rule space of the whole elementary cellular automata by combining the network topology and parameter analysis,and could provide support for automatic quantitative classification of complexity.First of all,using complex network visualization software to analyze the basic characteristics of state-mapping network of elementary cellular automata under different,and deriving the strong correlation of network nodes corresponding to different cell sizes in the integral domain of elementary cellular automata;secondly,accurately deriving the relevant properties of degree and degree distribution of some rules.Finally,summarizing common characteristics of different rules that can use such degree distribution calculation methods.