Robust state estimation for uncertain neural networks with time-varying delay
Robust state estimation for uncertain neural networks with time-varying delay
复制标题
DOI：
10.1109/tnn.2008.2000206
复制
发表时间：
2008-08-01
影响因子：
--
通讯作者：
Cao, Jinde
中科院分区：
文献类型：
--
作者：
Huang, He;Feng, Gang;Cao, Jinde
研究了一类具有时变时滞的不确定神经网络的鲁棒状态估计问题。假设参数不确定性是范数有界的。基于一种新的边界技术，给出了不确定延迟神经网络期望状态估计量存在的充分条件。该准则取决于时变延迟的大小和时变延迟的时间导数的大小。结果表明，这种神经网络的鲁棒状态估计器的设计可以通过求解线性矩阵不等式（LMI）来实现，并且可以使用一些标准的数值包来方便地实现。最后，给出了两个仿真实例，验证了所提方法的有效性。
The robust state estimation problem for a class of uncertain neural networks with time-varying delay is studied in this paper. The parameter uncertainties are assumed to be norm bounded. Based on a new bounding technique, a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks. The criterion is dependent on the size of the time-varying delay and on the size of the time derivative of the time-varying delay. It is shown that the design of the robust state estimator for such neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. Finally, two simulation examples are given to demonstrate the effectiveness of the developed approach.