IEEE Trans Neural Netw Learn Syst. 2017 May;28(5):1152-1163. doi: 10.1109/TNNLS.2016.2516030. Epub 2016 Feb 19.
In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. In order to cater for more realistic transmission process of the neural signals, we make the first attempt to introduce a set of stochastic variables to characterize the random fluctuations of system parameters. In the addressed neural network model, the delays among the interconnections are allowed to be different, which are more general than those in the existing literature. The incomplete information under consideration includes randomly occurring sensor saturations and quantizations. For the purpose of energy saving, an event-triggered state estimator is constructed and a sufficient condition is given under which the estimation error dynamics is exponentially ultimately bounded in the mean square. It is worth noting that the ultimate boundedness of the error dynamics is explicitly estimated. The characterization of the desired estimator gain is designed in terms of the solution to a certain matrix inequality. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed event-triggered state estimation scheme.
本文针对一类具有随机参数和不完整测量的离散时间多时滞神经网络，研究了事件触发状态估计问题。为了适应更实际的神经信号传输过程，我们首次尝试引入一组随机变量来描述系统参数的随机波动。在所研究的神经网络模型中，允许连接之间的延迟不同，这比现有文献中的延迟更加通用。所考虑的不完整信息包括随机发生的传感器饱和和量化。为了节能，构建了一个事件触发状态估计器，并给出了一个充分条件，使得估计误差动态在均方意义下指数最终有界。值得注意的是，误差动态的有界性是明确估计的。期望的估计器增益的特征化是通过求解一定的矩阵不等式来设计的。最后，通过数值仿真示例说明了所提出的事件触发状态估计方案的有效性。
