可激介质中复杂图案形成的CNN模型

Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI:10.1109/CNNA.1994.381657

S. Jankowski, R. Wanczuk

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引用次数: 4

摘要

本文提出了非线性离散细胞神经网络作为可激介质的模型。它可以看作是反应扩散方程的CNN解。这种方法将Gerhardt和Schuster(1989)的元胞自动化应用于CNN范式。结果表明，通过选择合适的模型参数，可以有效地获得大量的复杂图形(包括各种类型的螺旋波)。

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CNN models of complex pattern formation in excitable media

The paper presents the nonlinear discrete-time cellular neural networks as a model of excitable media. It can be considered as a CNN solution of a reaction-diffusion equation. This approach adapts the cellular automation of Gerhardt and Schuster (1989) to the CNN paradigm. It is shown that a large variety of complex patterns (including various types of spiral waves) can be efficiently obtained by the proper choice of the model parameters.<>

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Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)

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