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Nonfragile H State Estimation for Recurrent Neural Networks With Time-Varying Delays: On Proportional–Integral Observer Design

机译:非免费 h 具有时变延迟的经常性神经网络的状态估计:按比例积分观察器设计

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摘要

In this article, a novel proportional-integral observer (PIO) design approach is proposed for the nonfragile H-infinity state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped with more design freedom leading to better steady-state accuracy compared with the conventional Luenberger observer. The phenomena of randomly occurring gain variations, which are characterized by the Bernoulli distributed random variables with certain probabilities, are taken into consideration in the implementation of the addressed PIO. Attention is focused on the design of a nonfragile PIO such that the error dynamics of the state estimation is exponentially stable in a mean-square sense, and the prescribed H-infinity performance index is also achieved. Sufficient conditions for the existence of the desired PIO are established by virtue of the Lyapunov-Krasovskii functional approach and the matrix inequality technique. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed PIO design scheme.
机译:在本文中,提出了一种新的比例 - 积分观察者(PIO)设计方法,用于具有时变延迟的一类离散时间经常性神经网络的非自由型H-Infinity状态估计问题。与传统的Luenberger观察者相比,发达的PIO配备了更多的设计自由,导致更好的稳态精度。在寻址PIO的实现中考虑了随机发生的增益变化的现象,其特征在于具有某些概率的Bernoulli分布式随机变量。注意力集中在非自由PIO的设计上,使得状态估计的误差动态在平均方形感中是指数稳定的,并且还实现了规定的H-Infinity性能指标。通过Lyapunov-Krasovskii功能方法和基质不等式技术建立了所需PIO的充分条件。最后,提供了一种模拟示例以证明所提出的PIO设计方案的有效性。

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