基于采样数据的安全同步控制延迟合模糊惯性神经网络在欺骗攻击下
IEEE transactions on cybernetics
|February 24, 2026
概括
本研究引入了一个模糊采样数据安全控制器,以解决对延迟合模糊惯性神经网络 (FINNs) 的欺骗攻击. 该方法确保了这些网络的指数级同步,尽管存在敌对干扰.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 延迟合模糊惯性神经网络 (FINNs) 容易受到欺骗攻击.
- 在逆境条件下确保网络安全和同步是至关重要的.
研究的目的:
- 设计FINNs针对欺骗攻击的强有力的安全控制策略.
- 在欺骗性干扰下分析系统的行为,并保证指数级同步.
主要方法:
- 设计了一个模糊采样数据安全控制器.
- 使用了利亚普诺夫函数 (LKF) 和不等式技术.
- 线性矩阵不等式 (LMIs) 用于建立同步标准.
主要成果:
- 为分析受欺骗的闭环系统行为制定了一个理论框架.
- 成功建立了实现指数级同步的标准.
- 数字模拟证实了拟议的安全控制方法的有效性.
结论:
- 开发的安全控制方法有效地减轻了欺骗攻击.
- 延迟联的FINN的指数级同步甚至可以在逆境条件下实现.
- 该方法证明了在保护智能网络的实际应用性.
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