An error passivation approach to filtering for switched neural networks with noise disturbance
An error passivation approach to filtering for switched neural networks with noise disturbance
复制标题
DOI：
10.1007/s00521-010-0474-5
复制
发表时间：
2012-07
影响因子：
6
通讯作者：
C. Ahn
中科院分区：
文献类型：
--
作者：
C. Ahn
本文针对具有时滞和噪声干扰的切换Hopfield神经网络，利用误差钝化方法导出了一种新的无源指数滤波器。基于Lyapunov-Krasovskii稳定性理论、詹森不等式和线性矩阵不等式（LMI），建立了一个新的充分判据，使得滤波误差系统是指数稳定的，并且对于噪声干扰对输出误差是无源的.结果表明，所提出的开关无源滤波器的未知增益矩阵可以通过求解一组线性矩阵不等式来确定，这可以很容易地通过使用一些标准的数值软件包。最后给出了一个例子来说明所提出的开关无源滤波器的有效性。
In this paper, an error passivation approach is used to derive a new passive and exponential filter for switched Hopfield neural networks with time-delay and noise disturbance. Based on Lyapunov-Krasovskii stability theory, Jensen’s inequality, and linear matrix inequality (LMI), a new sufficient criterion is established such that the filtering error system is exponentially stable and passive from the noise disturbance to the output error. It is shown that the unknown gain matrix of the proposed switched passive filter can be determined by solving a set of LMIs, which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed switched passive filter.