对于离散时间延迟神经网络的可变增益冲动同步及其在数字安全通信中的应用
IEEE transactions on neural networks and learning systems
|October 10, 2023
概括
这项研究引入了一种新的双Lyapunov方法,用于稳定和同步离散时间延迟神经网络 (DDNNs) 与输入干扰. 可变增益控制器可以改善干扰排斥,并允许更长的脉冲间隔.
科学领域:
- 控制理论 控制理论
- 计算神经科学是一种神经科学.
- 网络科学 网络科学
背景情况:
- 离散时间延迟神经网络 (DDNN) 在建模复杂系统中至关重要.
- 冲动稳定和同步是具有挑战性的问题,特别是在输入通道干扰时.
- 现有的方法往往受到保守主义和有限的适应能力的影响.
研究的目的:
- 开发一种新的Lyapunov方法来分析冲动DDNN中的指数级输入到状态稳定性 (EISS).
- 设计有效的冲动控制器,以实现稳定和同步,并增强干扰排斥.
- 减少保守主义,提高对DDNN的控制策略的灵活性.
主要方法:
- 引入了一种新的双 Lyapunov 功能方法.
- 一对依赖时间的利亚普诺夫函数为冲动的DDNN而构建.
- 设计标准是使用线性矩阵不等式 (LMI) 来得出的.
主要成果:
- 与以前的技术相比,拟议的方法减少了保守主义.
- 设计了可变增益冲动控制器,提供更大的灵活性.
- 数字模拟表明,在干扰减弱和接受更长的脉冲间隔方面,性能优越.
- 通过数字信号和图像加密的应用来验证有效性.
结论:
- 双 Lyapunov 功能方法为分析和控制冲动 DDNN 提供了一个强大的工具.
- 可变增益控制器在存在干扰的情况下提高了稳定性和性能.
- 这些发现对安全通信和复杂系统控制有实际意义.
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