H∞控制分数顺序神经网络的不确定性,通过改进的记忆事件触发方案及其应用而受到欺骗攻击
K Asmiya Banu1, T Aparna1, M Mubeen Tajudeen2
1Department of Mathematics, Faculty of Science and Humanities, Dhanalakshmi Srinivasan University, Trichy 632 115, Tamil Nadu, India.
概括
本研究介绍了对分数顺序神经网络 (FONNs) 的增强记忆事件触发策略,以提高对欺骗攻击的安全性,同时节省网络带宽. 这种新方法确保了系统稳定性和H∞性能,并通过Chua验证验证.
科学领域:
- 控制系统工程 控制系统工程
- 网络安全 网络安全
- 人工神经网络的人工神经网络
背景情况:
- 分数级神经网络 (FONNs) 容易受到欺骗攻击和外部干扰.
- 现有的控制策略往往消耗大量的网络带宽.
- 在受到攻击的不确定的FONNs中确保稳定性和性能是具有挑战性的.
研究的目的:
- 为不确定的FONNs开发一个改进的内存事件触发的控制策略.
- 提高通信安全性和节省网络带宽.
- 为了保证H∞的性能和在欺骗攻击下异常稳定性.
主要方法:
- 一个增强的内存事件触发框架,利用最近发布的包.
- 开发一种新的FONN模型,其中包含欺骗攻击的影响.
- 用于稳定性分析的利亚普诺夫-克拉索夫斯基函数 (LKF) 的构造.
- 使用线性矩阵不等式 (LMIs) 来推导稳定性标准和H∞性能.
主要成果:
- 拟议的战略有效地提高了对欺骗攻击的通信安全性.
- 与传统方法相比,大大节省了网络带宽.
- 实现了对称稳定性和保证的H∞性能.
- 控制器增益和权重矩阵可以通过LMI来确定.
结论:
- 增强的内存事件触发策略为FONNs的安全和高效控制提供了强大的解决方案.
- 该方法通过模拟进行验证,包括一个Chua的二极管电路系统.
- 这项工作有助于安全控制复杂的神经网络系统.
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