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原文連結
論文資訊
- 類型：已發表論文
- 日期：2008
摘要
Intrinsic computation refers to how dynamical systems store, structure, and transform historical and spatial 資訊. By graphing a measure of structural complexity against a measure of randomness, complexity-熵 diagrams display the different kinds of intrinsic computation across an entire class of systems. Here, we use complexity-熵 diagrams to analyze intrinsic computation in a broad array of deterministic 非線性 and linear 隨機 processes, including maps of the interval, 細胞自動機, and Ising spin systems in one and two dimensions, 馬可夫 chains, and probabilistic minimal finite-state machines. Since complexity-熵 diagrams are a function only of observed configurations, they can be used to compare systems without reference to system coordinates or parameters. It has been known for some time that in special c
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