Chance-Constrained $H_{∞}$ State Estimation for Recursive Neural Networks Under Deception Attacks and Energy Constraints: The Finite-Horizon Case
Chance-Constrained $H_{∞}$ State Estimation for Recursive Neural Networks Under Deception Attacks and Energy Constraints: The Finite-Horizon Case
复制标题
欺骗攻击和能量约束下递归神经网络的机会约束 $H_{â}$ 状态估计：有限视野情况
DOI：
10.1109/tnnls.2021.3137426
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发表时间：
2022
影响因子：
10.4
通讯作者：
Xia Zhao
中科院分区：
文献类型：
--
作者：
Fanrong Qu;Engang Tian;Xia Zhao
本文研究了一类具有测量退化和随机欺骗攻击的时变神经网络的机会约束状态估计问题。提出了一种新的能量约束欺骗攻击模型，该模型将攻击的发生和释放的伪造数据包的选择都是随机的，并对欺骗攻击的能量进行了引入、计算和定量分析。该问题的主要目的是设计出既满足系统误差动力学的预先概率约束又保证性能的估计器。然后，通过求解一类递推不等式约束下的最小化问题，得到了估计量增益的显式表达式。最后，通过一个数值算例和一个实际的三罐系统，验证了所提估计方法的正确性和有效性。
In this article, the chance-constrainedstate estimation problem is investigated for a class of time-varying neural networks subject to measurements degradation and randomly occurring deception attacks. A novel energy-constrained deception attack model is proposed, in which both the occurrence of the attack and the selection of released faked packet are random and the energy of the deception attack is introduced, calculated, and analyzed quantitatively. The main purpose of the addressed problem is to design anestimator such that the prefixed probabilistic constraints of the system error dynamics are satisfied and theperformance is also ensured. Subsequently, the explicit expression of the estimator gains is derived by solving a minimization problem subjected to certain recursive inequality constraints. Finally, a numerical example and a practical three-tank system are utilized to demonstrate the correctness and effectiveness of the proposed estimation scheme.