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在分数顺序的HNN中,memristor诱导的超级混乱,多滚动和极端多稳定性:图像加密和FPGA实现
Xinxin Kong1, Fei Yu1, Wei Yao1
1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China.
概括
分数顺序记忆的霍普菲尔德神经网络 (FOMHNNs) 表现出复杂的行为,如超混沌和极端多稳定性. 这项研究引入了一种新的FOMHNN,用于高效和安全的图像加密,展示了强大的安全性和硬件可实现性.
科学领域:
- 非线性动力学是一种非线性动力学.
- 混沌理论 混沌理论
- 记忆系统 记忆系统
背景情况:
- 分数顺序差异化 (FOD) 提供了比整数顺序系统更高的复杂性和准确性,用于模拟现实世界的现象.
- 记忆器,特别是分数顺序类型,对于开发具有非挥发性和极端多稳定性等独特属性的高级神经网络模型至关重要.
研究的目的:
- 提出两个新的分数顺序的memristor,其中一个表现出极端的多稳定性,局部活动和非挥发性.
- 建立一个新的5D分数顺序记忆的霍普菲尔德神经网络 (FOMHNN) 通过模拟神经元的Autapses和电磁感应.
- 根据拟议的FOMHNN.设计一个高效和安全的图像加密方案.
主要方法:
- 开发和分析两种类型的分数顺序记忆器.
- 构建一个5D分数级记忆的霍普菲尔德神经网络 (FOMHNN).
- 动态模拟用于探索复杂的行为,包括超混沌和极端多稳定性.
- 图像加密方案的设计和安全分析.
- 现场可编程门阵列 (FPGA) 实现了FOMHNN和加密算法.
主要成果:
- 一个拟议的分数顺序的memristor证明了极端的多稳定性,局部活动和非挥发性.
- 小说5D FOMHNN表现出丰富的动态:超级混乱,多滚动,极端多稳定性,以及订单减少诱导的"超时"行为.
- 图像加密方案显示高随机性 (7.9995) 和优秀的纯文本敏感性 (NCPR 99.6095%,UACI 33.4671%) 没有扩散或编码.
- 在FPGA上成功实现FOMHNN和图像加密的硬件实现.
结论:
- 这项研究提出了一种具有前所未有的丰富动态行为的新型FOMHNN.
- 开发的FOMHNN为高效和安全的图像加密系统提供了坚实的基础.
- FPGA的实施突出了小数序神经网络在安全通信中的实用性和新性.
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