Ahn Choon Ki
Seoul National University of Science & Technology, Seoul, Korea.
Eur Phys J E Soft Matter. 2011 Nov;34(11):122. doi: 10.1140/epje/i2011-11122-8. Epub 2011 Nov 21.
This paper deals with the delay-dependent exponentially convergent state estimation problem for delayed switched neural networks. A set of delay-dependent criteria is derived under which the resulting estimation error system is exponentially stable. It is shown that the gain matrix of the proposed state estimator is characterised in terms of the solution to a set of linear matrix inequalities (LMIs), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.
本文研究了时滞切换神经网络的时滞依赖指数收敛状态估计问题。推导了一组时滞依赖准则，在该准则下，所得估计误差系统是指数稳定的。结果表明，所提出的状态估计器的增益矩阵可以通过一组线性矩阵不等式（LMI）的解来表征，这些不等式可以使用一些标准数值软件包轻松检验。给出了一个示例来说明所提出的状态估计器的有效性。
