Reliable filter design for discrete-time neural networks with Markovian jumping parameters and time-varying delay
Reliable filter design for discrete-time neural networks with Markovian jumping parameters and time-varying delay
复制标题
具有马尔可夫跳跃参数和时变延迟的离散时间神经网络的可靠滤波器设计
DOI：
10.1016/j.jfranklin.2020.02.039
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Chu Yuming
中科院分区：
文献类型：
--
作者：
Xia Weifeng;Xu Shengyuan;Lu Junwei;Zhang Zhengqiang;Chu Yuming
研究了具有马尔可夫跳变参数和时变时滞的离散神经网络的可靠滤波器设计问题。首先，基于一个矩阵不等式，建立了一个新的充分条件，该条件保证存在可靠滤波器，使得滤波误差系统是随机稳定的和扩展耗散的.其次，提出了一个保守性较小的神经网络稳定性判据。然后，滤波器的设计问题的可解性条件的线性矩阵不等式（LMI）。最后，通过三个数值例子说明了所提出的滤波器设计方案的有效性和优越性。
This paper considers the problem of reliable filter design for discrete-time neural networks subject to Markovian jumping parameters and time-varying delay. Firstly, based on a matrix inequality, a new sufficient condition, which guarantees the existence of a reliable filter such that the resulting filtering error system is stochastically stable and extended dissipative, is established. Second, a less conservative stability criterion for neural networks is proposed. Then, the condition for the solvability of the filter design problem is given in terms of linear matrix inequalities (LMIs). Finally, three numerical examples are given to illustrate the effectiveness and advantages of the proposed filter design scheme.