二值锯齿和锯齿与牛补丁细胞神经网络耦合的动态分析

Mian Wang, L. Min, Min Li
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引用次数: 0

摘要

自然界充满了生物、化学、物理和社会系统中出现的复杂模式。细胞神经网络(cnn)可以产生类似于自然界中发现的模式,这意味着cnn可以用作描述自然界中某些系统的原型。Chua等人引入的二进制锯齿和牛斑cnn可以从任意随机初始模式生成锯齿和牛斑共存的模式。为了研究二值锯齿形和奶牛patch cnn的特点,本研究引入了模式像素的固有(终)主动、固有(终)被动和固有(终)中性的概念,提出了二值锯齿形和奶牛patch cnn的全局任务规则和局部规则,并建立了一组定理。通过三个仿真实例验证了理论结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic analysis of coupled Binary Sawtooth and Sawtooth and Cow patch cellular neural networks
Nature abounds with complex patterns emerging from biological, chemical, physical and social systems. Cellular Neural Networks (CNNs) may produce patterns similar to those found in nature, which implies that CNNs may be use as protoypes to describe some systems in nature. The Binary Sawtooth and Cow patch CNNs introduced by Chua et al. can generate pattern that sawtooths and cow patches coexist from any random initial pattern. In order to investigate the characteristics of the Binary Sawtooth and Cow patch CNNs, this study introduces concepts of so-called inherent (final) active, inherent (final) passive, and inherent (final) neutral for pattern pixels, and proposes Global Task and Local Rules of the Binary Sawtooth and Cow patch CNNs, and establishes a set of theorems. Three simulation examples have been carried out to verily the effectiveness of theoretical results.
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