恒星记忆神经网络:动态分析,电路实现和应用在彩色加密系统中
Sen Fu1,2,3, Zhengjun Yao1, Caixia Qian1,2
1College of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
这项研究介绍了一种新的恒星记忆神经网络 (SMNN) 模型,揭示了它对复杂的混乱动态和多滚动吸引力的能力. 这项研究还提出了一个基于这个SMNN的安全图像加密方案.
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:
- 记忆神经网络 (MNN) 由于其独特的特性而被广泛研究.
- 然而,星结构记忆神经网络 (SMNNs) 仍然未被探索.
- 研究新型网络拓对于推进多重网络应用至关重要.
研究的目的:
- 提出和分析一个新的恒星记忆神经网络 (SMNN) 模型.
- 探索SMNN的混乱动态和多滚动吸引器生成能力.
- 开发和验证使用SMNN的安全图像加密方案.
主要方法:
- 使用霍普菲尔德神经网络和流量控制的memristor开发了一个新的SMNN模型.
- 使用了数字分析技术,包括分叉图,利亚普诺夫指数和相位图.
- 使用MULTISIM进行模拟模拟电路验证了理论发现.
主要成果:
- SMNN表现出复杂的动态行为,包括混乱和多滚动吸引器.
- 吸引器的数量和位置可以通过调整memristor参数和初始值来控制.
- 实现的模拟电路证实了SMNN的理论预测.
结论:
- 拟议的SMNN模型展示了丰富的混乱动态和可控制的多滚动吸引器.
- 该SMNN适用于安全图像加密等应用,由开发的加密系统证明了这一点.
- 这项研究为探索恒星拓学的记忆神经网络开辟了新的途径.
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