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Synchronization and State Estimation of a Class of Hierarchical Hybrid Neural Networks With Time-Varying Delays

机译：一类具有时变时滞的多层混合神经网络的同步和状态估计

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摘要

This paper addresses the problems of synchronization and state estimation for a class of discrete-time hierarchical hybrid neural networks (NNs) with time-varying delays. The hierarchical hybrid feature consists of a higher level nondeterministic switching and a lower level stochastic switching. The latter is used to describe the NNs subject to Markovian modes transitions, whereas the former is of the average dwell-time switching regularity to model the supervisory orchestrating mechanism among these Markov jump NNs. The considered time delays are not only time-varying but also dependent on the mode of NNs on the lower layer in the hierarchical structure. Despite quantization and random data missing, the synchronized controllers and state estimators are designed such that the resulting error system is exponentially stable with an expected decay rate and has a prescribed disturbance attenuation level. Two numerical examples are provided to show the validity and potential of the developed results.
机译：本文解决了一类具有时变时滞的离散时间分层混合神经网络（NNs）的同步和状态估计问题。分层混合功能包括较高级别的不确定开关和较低级别的随机开关。后者用于描述经受马尔可夫模式转变的神经网络，而前者具有平均停留时间切换规律，以在这些马尔可夫跳跃神经网络之间建立监督协调机制的模型。所考虑的时间延迟不仅随时间变化，而且还取决于分层结构中较低层上的NN的模式。尽管缺少量化和随机数据，但仍设计了同步控制器和状态估计器，以使所得的误差系统在具有预期衰减率的情况下呈指数稳定，并具有规定的干扰衰减水平。提供了两个数值示例来说明所开发结果的有效性和潜力。

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