IEEE Trans Neural Netw Learn Syst. 2016 Jan;27(1):77-88. doi: 10.1109/TNNLS.2015.2411734.
This paper is concerned with event-triggered generalized dissipativity filtering for a neural network (NN) with a time-varying delay. The signal transmission from the NN to its filter is completed through a communication channel. It is assumed that the network measurement of the NN is sampled periodically. An event-triggered communication scheme is introduced to design a suitable filter such that precious communication resources can be saved significantly while certain filtering performance can be ensured. On the one hand, the event-triggered communication scheme is devised to select only those sampled signals violating a certain threshold to be transmitted, which directly leads to saving of precious communication resources. On the other hand, the filtering error system is modeled as a time-delay system closely dependent on the parameters of the event-triggered scheme. Based on this model, a suitable filter is designed such that certain filtering performance can be ensured, provided that a set of linear matrix inequalities are satisfied. Furthermore, since a generalized dissipativity performance index is introduced, several kinds of event-triggered filtering issues, such as H∞ filtering, passive filtering, mixed H∞ and passive filtering, (Q,S,R) -dissipative filtering, and L2 - L∞ filtering, are solved in a unified framework. Finally, two examples are given to illustrate the effectiveness of the proposed method.
本文研究了时变时滞神经网络的事件触发广义耗散滤波问题。神经网络到滤波器的信号传输通过通信信道完成。假设神经网络的网络测量是周期性采样的。引入了一种事件触发通信方案，设计了一个合适的滤波器，在保证一定滤波性能的同时，可以显著节省宝贵的通信资源。一方面，设计事件触发通信方案选择仅那些违反一定阈值的采样信号进行传输，这直接导致宝贵的通信资源得以节省。另一方面，滤波误差系统被建模为一个与事件触发方案的参数密切相关的时滞系统。基于该模型，设计了一个合适的滤波器，在满足一组线性矩阵不等式的条件下，可以保证一定的滤波性能。此外，由于引入了广义耗散性能指标，可以在统一框架内解决几种事件触发滤波问题，如 H∞滤波、无源滤波、混合 H∞和无源滤波、(Q,S,R)-耗散滤波和 L2-L∞滤波。最后，给出了两个实例来说明所提出方法的有效性。
