In this paper, the issue of robust distributed state estimation is investigated for Markov coupled neural networks in the discrete-time domain. Fully considering network-induced phenomena, the signal quantization and sensor saturation existed in actual measurement are investigated in a unified framework through the Kronecker delta function. Moreover, a Markov chain is used to describe the structural variations in the addressed systems. The main attention of this paper is devoted to designing a mode-dependent estimator to estimate the system states through available output measurements, which ensures that the resulting system is stochastically stable and satisfies strictly dissipative property concurrently. By applying Lyapunov stability theory and a modified matrix decoupling method, some sufficient criteria are derived to obtain an explicit expression of the mode-dependent estimator. Finally, an example is presented to elucidate the validity of the proposed method.

本文研究了离散时域马尔可夫耦合神经网络的鲁棒分布状态估计问题。充分考虑网络引起的现象，通过Kronecker德尔塔函数在统一的框架内研究了实际测量中存在的信号量化和传感器饱和。此外，马尔可夫链用于描述所解决系统中的结构变化。本文的主要重点是设计一种依赖于模式的估计器，以通过可用的输出测量来估计系统状态，从而确保所得系统是随机稳定的，并且同时满足严格的耗散特性。通过应用Lyapunov稳定性理论和改进的矩阵去耦方法，得出一些足够的标准以获得依赖于模式的估计器的显式表达式。最后，通过一个例子来说明所提方法的有效性。