Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements
Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements
复制标题
DOI：
10.1109/tnnls.2016.2516030
复制
发表时间：
2017-05
影响因子：
10.4
通讯作者：
Bo Shen;Zidong Wang;Hong Qiao
中科院分区：
文献类型：
--
作者：
Bo Shen;Zidong Wang;Hong Qiao
研究了一类具有随机参数和不完全测量值的离散多延迟神经网络的事件触发状态估计问题。为了适应更真实的神经信号传递过程，我们首次尝试引入一组随机变量来表征系统参数的随机波动。在寻址的神经网络模型中，允许互联之间的延迟不同，这比现有文献中的延迟更普遍。考虑的不完全信息包括随机发生的传感器饱和和量化。为了节省能量，构造了一个事件触发状态估计器，并给出了估计误差动态最终在均方有指数界的充分条件。值得注意的是，误差动力学的最终有界性得到了显式估计。期望估计器增益的表征是根据某矩阵不等式的解来设计的。最后，给出了一个数值仿真实例，验证了所提出的事件触发状态估计方案的有效性。
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.