IEEE Trans Neural Netw Learn Syst. 2022 Jul;33(7):2791-2800. doi: 10.1109/TNNLS.2020.3045146. Epub 2022 Jul 6.
This article proposes a new Luenberger-type state estimator that has parameterized observer gains dependent on the activation function, to improve the H state estimation performance of the static neural networks with time-varying delay. The nonlinearity of the activation function has a significant impact on stability analysis and robustness/performance. In the proposed state estimator, a parameter-dependent estimator gain is reconstructed by using the properties of the sector nonlinearity of the activation functions that are represented as linear combinations of weighting parameters. In the reformulated form, the constraints of the parameters for the activation function are considered in terms of linear matrix inequalities. Based on the Lyapunov-Krasovskii function and the improved reciprocally convex inequality, enhanced conditions for designing a new state estimator that guarantees H performance are derived through a parameterization technique. The compared results with recent studies demonstrate the superiority and effectiveness of the presented method.
本文提出了一种新的 Luenberger 型状态估计器，其观测器增益参数化依赖于激活函数，以改善时变时滞静态神经网络的 H 状态估计性能。激活函数的非线性对稳定性分析和鲁棒性/性能有重大影响。在提出的状态估计器中，通过使用激活函数的扇形非线性的特性，通过使用权重参数的线性组合来重构参数相关的估计器增益。在重新格式化的形式中，根据线性矩阵不等式来考虑激活函数的参数约束。基于 Lyapunov-Krasovskii 函数和改进的互凸不等式，通过参数化技术推导出了设计新状态估计器的增强条件，以保证 H 性能。与最近的研究结果相比，所提出的方法具有优越性和有效性。
