Liu Jinliang, Tang Jia, Fei Shumin
College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, PR China; School of Automation, Southeast University, Nanjing, Jiangsu 210096, PR China.
Department of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, PR China.
Neural Netw. 2016 Oct;82:39-48. doi: 10.1016/j.neunet.2016.06.006. Epub 2016 Jul 11.
This paper is concerned with H∞ filter design for a class of neural network systems with event-triggered communication scheme and quantization. Firstly, a new event-triggered communication scheme is introduced to determine whether or not the current sampled sensor data should be broadcasted and transmitted to quantizer, which can save the limited communication resource. Secondly, a logarithmic quantizer is used to quantify the sampled data, which can reduce the data transmission rate in the network. Thirdly, considering the influence of the constrained network resource, we investigate the problem of H∞ filter design for a class of event-triggered neural network systems with quantization. By using Lyapunov functional and linear matrix inequality (LMI) techniques, some delay-dependent stability conditions for the existence of the desired filter are obtained. Furthermore, the explicit expression is given for the designed filter parameters in terms of LMIs. Finally, a numerical example is given to show the usefulness of the obtained theoretical results.
本文关注一类具有事件触发通信方案和量化的神经网络系统的H∞滤波器设计。首先，引入一种新的事件触发通信方案来确定当前采样的传感器数据是否应被广播并传输到量化器，这可以节省有限的通信资源。其次，使用对数量化器对采样数据进行量化，这可以降低网络中的数据传输速率。第三，考虑受限网络资源的影响，我们研究一类具有量化的事件触发神经网络系统的H∞滤波器设计问题。通过使用李雅普诺夫泛函和线性矩阵不等式（LMI）技术，得到了存在期望滤波器的一些时滞依赖稳定性条件。此外，根据LMI给出了设计滤波器参数的显式表达式。最后，给出一个数值例子来说明所获理论结果的有效性。
