论文标题
一类复发性神经网络的增量输入到国家稳定性条件
An incremental input-to-state stability condition for a generic class of recurrent neural networks
论文作者
论文摘要
本文提出了一种新的足够条件，可以使一类复发性神经网络（RNN）类别的输入到状态稳定性。将既定状态与文献中可用的其他条件进行了比较，这表明不那么保守。此外，它可以应用于增量输入到状态稳定的基于RNN的控制系统的设计，从而对某些特定的RNN体系结构产生线性矩阵不等式约束。还研究了对所考虑的系统类别的非线性观察者的配方，以及具有明确积分作用的控制方案的设计。理论结果通过在引用的非线性系统上的模拟验证。
This paper proposes a novel sufficient condition for the incremental input-to-state stability of a generic class of recurrent neural networks (RNNs). The established condition is compared with others available in the literature, showing to be less conservative. Moreover, it can be applied for the design of incremental input-to-state stable RNN-based control systems, resulting in a linear matrix inequality constraint for some specific RNN architectures. The formulation of nonlinear observers for the considered system class, as well as the design of control schemes with explicit integral action, are also investigated. The theoretical results are validated through simulation on a referenced nonlinear system.
