State Estimation of Discrete-Time Switched Neural Networks With Multiple Communication Channels
研究了具有模态持久驻留时间切换和混合时滞的离散时间切换神经网络的状态估计问题，设计了依赖模态的滤波器，使滤波误差系统指数均方稳定并保证性能指标，且更多信道可提升性能。
In this paper, the state estimation problem for a class of discrete-time switched neural networks with modal persistent dwell time (MPDT) switching and mixed time delays is investigated. The considered switching law, not only generalizes the commonly studied dwell-time (DT) and average DT (ADT) switchings, but also further attaches mode-dependency to the persistent DT (PDT) switching that is shown to be more general. Multiple communication channels, which include one primary channel and multiredundant channels, are considered to coexist for the state estimation of underlying switched neural networks. The desired mode-dependent filters are designed such that the resulting filtering error system is exponentially mean-square stable with a guaranteed nonweighted generalized 112 performance index. It is verified that better filtering performance index can be achieved as the number of channels to be used increases. The potential and effectiveness of the developed theoretical results are demonstrated via a numerical example.