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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on > $H_{infty }$ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements
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$H_{infty }$ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements

机译： $ H_ {infty} $ 具有多个缺失测量的神经网络类型离散时间延迟系统的状态估计

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摘要

This paper investigates the state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur’e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static nonlinear operator. The missing phenomenon commonly existing in measurements is assumed to occur randomly by introducing mutually individual random variables satisfying certain kind of probability distribution. Throughout this paper, first a Luenberger-like estimator based on the imperfect output data is constructed to obtain the immeasurable system states. Then, by virtue of Lyapunov stability theory and stochastic method, the performance of the estimation error dynamical system (augmented system) is analyzed. Based on the analysis, the estimator gains are deduced such that the augmented system is globally mean square stable. In this paper, both the variation range and distribution probability of the time delay are incorporated into the control laws, which allows us to not only have more accurate models of the real physical systems, but also obtain less conservative results. Finally, three illustrative examples are provided to validate the proposed control laws.
机译：本文研究了一类具有随机时变时滞和多次缺失测量的神经网络类型的离散时间非线性系统的状态估计问题。这些非线性系统包括递归神经网络，复杂网络系统，Lur'e系统等，它们可以由包含线性动态系统和静态非线性算子的统一模型来描述。通过引入相互满足某些概率分布的随机变量，可以假定测量中普遍存在的缺失现象是随机发生的。在整个本文中，首先基于不完美的输出数据构建类似Luenberger的估计器，以获取不可测量的系统状态。然后，利用李雅普诺夫稳定性理论和随机方法，分析了估计误差动态系统（增强系统）的性能。基于该分析，推导估计器增益，以使增强系统全局均方稳定。本文将时滞的变化范围和分布概率都纳入了控制律中，这不仅使我们能够更准确地模拟实际物理系统，而且获得的保守性也较小。最后，提供了三个说明性示例来验证建议的控制律。

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