随机记忆神经网络的有限时间和固定时间自触发同步以及安全通信中的应用
Mingxin Wang1, Song Zhu1, Weiwei Luo1
1School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
概括
这项研究探讨了随机干扰和歇斯底里等环境因素下的记忆神经网络 (MNN). 它提出了确保安全通信和网络稳定的同步策略.
科学领域:
- 计算神经科学
- 非线性动力学
- 控制理论
背景情况:
- 记忆神经网络 (MNN) 呈现出由环境因素影响的复杂动态.
- 随机干扰,外部输入和执行器歇斯底里显著影响MNN性能.
- 安全的通信方案需要强大且同步的网络行为.
研究的目的:
- 调查随机干扰,外部输入和执行器歇斯底里对MNN的综合影响.
- 使用歇斯底里和外部输入引入驱动响应随机 MNNs (SMNNs).
- 开发SMNN和安全通信系统的同步策略.
主要方法:
- 引入包含歇斯底里和外部输入的随机MNN (SMNN).
- 同步定义的制定:在概率上是异面的,有限的时间和固定的时间.
- 在控制器设计中应用自触发控制和莱普诺夫稳定性理论.
主要成果:
- 设计用于实现SMNN的有限时间和固定时间同步的控制器.
- 确保安全通讯系统的异常同步.
- 通过模拟和比较实验证明拟议策略的有效性.
结论:
- 建议的同步策略有效地管理环境影响下的SMNN的复杂动态.
- 开发的方法通过同步的网络状态来确保可靠的安全通信.
- 这项研究有助于在动态环境中基于MNN的系统的稳定性和安全性.
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