Zhang Ruimei, Zeng Deqiang, Park Ju H, Liu Yajuan, Zhong Shouming
IEEE Trans Neural Netw Learn Syst. 2018 Dec;29(12):6385-6395. doi: 10.1109/TNNLS.2018.2836339. Epub 2018 Jun 5.
This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.
本文研究了通过量化采样数据控制实现具有异质时变延迟（HTVDs）的惯性神经网络（INNs）的同步问题。该控制方案考虑了量化和可变采样的通信限制，首次用于解决INNs的同步问题。构造了一种新颖的Lyapunov-Krasovskii泛函（LKF）来同步误差系统。与现有基于所有HTVDs最大上界的LKF相比，所提出的LKF更具优势，因为它可以充分利用每个HTVD上下界的信息。基于LKF和一种新的积分不等式技术，推导了保守性更低的同步准则。通过求解一组线性矩阵不等式设计了所需的量化采样数据控制器。最后，给出了一个数值例子来说明所提结果的有效性和保守性降低情况。
