对FN-HR神经网络的动态分析,加上基于斐波纳契Q矩阵的可二位式记忆器和加密应用程序
Junwei Sun1, Chuangchuang Li1, Yanfeng Wang1
1School of Electrical Information and Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002 China.
Cognitive neurodynamics
|December 16, 2024
概括
本研究介绍了一种基于memristor的神经网络,用于复杂的动态,并提出了一种安全的图像加密算法. 记忆器神经网络表现出强大的安全性,可以抵御破解的尝试.
科学领域:
- 神经科学是一个神经科学.
- 复杂的系统复杂的系统.
- 密码学 密码学 密码学 密码学
背景情况:
- 记忆器设备为神经形态计算提供了独特的特性.
- 结合的神经网络表现出与生物系统相关的复杂动态.
- 图像加密需要强大的和安全的算法.
研究的目的:
- 提出一个新的共弦过度模拟器模型.
- 使用这个memristor模型构建和分析一个合的神经网络.
- 开发基于memristor神经网络的安全图像加密算法.
主要方法:
- 开发了一种具有可二位式非对称hysteresis循环的共弦过波式memristor模型.
- 使用Fitzhugh-Nagumo和Hindmarsh-Rose模型构建了一个合的神经网络.
- 采用数值分析,局部吸引子盆地,利亚普诺夫指数地图和光谱地图进行表征.
主要成果:
- 结合的memristor神经网络表现出复杂的动态和大量的平衡点.
- 同时存在的放电行为是使用局部吸引子盆地确定.
- 拟议的图像加密算法展示了高安全性和抗攻击性.
结论:
- 拟议的memristor模型和合的神经网络为探索复杂动态提供了一个平台.
- 与memristor合的神经网络为先进的计算和安全应用提供了基础.
- 集成的图像加密算法是有效和安全的.
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