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首页> 美国政府科技报告 >Statistical Properties of Cellular Automata in the Context of Learning and Recognition: Part 2, Inverting Local Structure Theory Equations to Find Cellular Automata with Specified Properties
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Statistical Properties of Cellular Automata in the Context of Learning and Recognition: Part 2, Inverting Local Structure Theory Equations to Find Cellular Automata with Specified Properties

机译：学习和识别背景下元胞自动机的统计特性：第二部分，反演局部结构理论方程，找出具有特定属性的元胞自动机

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This is the second of two lectures. In the first lecture the map from a cellular automaton to a sequence of analytical approximations called the local structure theory was described. In this lecture the inverse map from approximation to the class of cellular automata approximated is constructed. The key matter is formatting the local structure theory equations in terms of block probability estimates weighted by coefficients. The inverse mapping relies on this format. Each possible assignment of values to the coefficients defines a class of automata with related statistical properties. It is suggested that these coefficients serve to smoothly parameterize the space of cellular automata. By varying the values of the parameters a cellular automaton network may be designed so that it has a specified invariant measure. If an invariant measure is considered a ''memory'' of the network, then this variation of parameters to specify the invariant measure must be considered ''learning.'' It is important to note that in this view learning is not the storage of patterns in a network, but rather the tailoring of the dynamics of a network. 7 figs. (ERA citation 14:016868)

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