Abstract This article deals with the set-membership state estimation problem for a class of artificial neural networks subject to time-delays and mixed malicious attacks. Both Denial-of-Service (DoS) and deception attacks are taken into consideration. The objective of the addressed problem is to design the state estimation algorithm for the artificial neural networks under investigation in spite of the existence of the malicious mixed attacks. By means of the set-membership approach in combination with certain convex optimization algorithm, the sufficient condition is established for the existence of the desired state estimator in terms of the solvability of a recursive matrix inequality. The resulting state estimation error is confined within certain pre-specified ellipsoidal region. An optimization problem is then formulated with the purpose of seeking the filtering parameters guaranteeing the locally optimal performance. Finally, the developed theoretical results are verified via an illustrative numerical example.

中文翻译：

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一类受混合攻击的人工神经网络的状态估计：一种集合成员方法

摘要 本文讨论了一类受时间延迟和混合恶意攻击影响的人工神经网络的集合成员状态估计问题。拒绝服务 (DoS) 和欺骗攻击都被考虑在内。所解决问题的目标是为正在研究的人工神经网络设计状态估计算法，尽管存在恶意混合攻击。通过集合隶属法结合一定的凸优化算法，就递归矩阵不等式的可解性，建立了期望状态估计量存在的充分条件。由此产生的状态估计误差被限制在某些预先指定的椭球区域内。然后制定优化问题，目的是寻找保证局部最优性能的过滤参数。最后，通过一个说明性的数值例子验证了所开发的理论结果。