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引用次数: 78

摘要

CNN的模板系数(权重)可以通过设计或学习找到,从而获得理想的性能;“通过设计”是指可以将想要执行的功能转化为一组局部动态规则,而“通过学习”是完全基于成对的输入和相应的输出信号,它们之间的关系可能过于复杂,无法明确地制定局部规则。这里给出了适用于cnn的设计和学习方法的概述,这些方法有时并不能明显区分。具体的硬件实现带来的技术约束和具体的应用和系统嵌入带来的实际约束都在影响着设计和学习
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Design and learning with cellular neural networks
The template coefficients (weights) of a CNN, which will give a desired performance, can either be found by design or by learning; "By design" means, that the desired function to be performed could be translated into a set of local dynamic rules, while "by learning" is based exclusively on pairs of input and corresponding output signals, the relationship of which may be by far too complicated for the explicit formulation of local rules. An overview of design and learning methods applicable to CNNs, which sometimes are not clearly distinguishable,is given here. Both technological constraints imposed by specific hardware implementation and practical constraints caused by the specific application and system embedding are influencing design and learning.<>
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