IEEE Trans Neural Netw Learn Syst. 2016 Feb;27(2):459-70. doi: 10.1109/TNNLS.2015.2412676. Epub 2015 Mar 25.
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 H∞ disturbance attenuation level. Two numerical examples are provided to show the validity and potential of the developed results.
本文针对一类具有时变时滞的离散时间分层混合神经网络（NN）的同步和状态估计问题进行了研究。分层混合特性包括高层非确定性切换和底层随机切换。后者用于描述受马尔可夫模式转换影响的神经网络，而前者则采用平均驻留时间切换规则来模拟这些马尔可夫跳跃神经网络之间的监督协调机制。所考虑的时滞不仅是时变的，而且还依赖于分层结构中下层神经网络的模式。尽管存在量化和随机数据丢失，仍设计了同步控制器和状态估计器，以使所得到的误差系统具有期望的衰减率和给定的 H∞干扰衰减水平，呈指数稳定。提供了两个数值示例，以验证所提出结果的有效性和潜力。
