In this brief, a new outlier-resistant state estimation (SE) problem is addressed for a class of recurrent neural networks (RNNs) with mixed time-delays. The mixed time delays comprise both discrete and distributed delays that occur frequently in signal transmissions among artificial neurons. Measurement outputs are sometimes subject to abnormal disturbances (resulting probably from sensor aging/outages/faults/failures and unpredictable environmental changes) leading to measurement outliers that would deteriorate the estimation performance if directly taken into the innovation in the estimator design. We propose to use a certain confidence-dependent saturation function to mitigate the side effects from the measurement outliers on the estimation error dynamics (EEDs). Through using a combination of Lyapunov-Krasovskii functional and inequality manipulations, a delay-dependent criterion is established for the existence of the outlier-resistant state estimator ensuring that the corresponding EED achieves the asymptotic stability with a prescribed H∞ performance index. Then, the explicit characterization of the estimator gain is obtained by solving a convex optimization problem. Finally, numerical simulation is carried out to demonstrate the usefulness of the derived theoretical results.

中文翻译：

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混合时滞循环神经网络的抗异常值远程状态估计


在本文中，针对一类具有混合时滞的循环神经网络 (RNN) 解决了一个新的抗离群状态估计 (SE) 问题。混合时间延迟包括在人工神经元之间的信号传输中频繁出现的离散延迟和分布式延迟。测量输出有时会受到异常干扰（可能由传感器老化/断电/故障/故障和不可预测的环境变化引起），从而导致测量异常值，如果直接将其纳入估算器设计的创新中，则会降低估算性能。我们建议使用某种依赖于置信度的饱和函数来减轻测量异常值对估计误差动态（EED）的副作用。通过结合Lyapunov-Krasovskii泛函和不等式操作，建立了抗离群状态估计器存在性的时滞相关准则，确保相应的EED在指定的H∞性能指标下实现渐近稳定性。然后，通过求解凸优化问题获得估计器增益的显式表征。最后，进行数值模拟以证明所得出的理论结果的有用性。