Zhang Huaguang, Liu Zhenwei, Huang Guang-Bin
School of Information Science and Engineering, Northeastern University, Shenyang 110004, China.
IEEE Trans Syst Man Cybern B Cybern. 2010 Dec;40(6):1480-91. doi: 10.1109/TSMCB.2010.2040274. Epub 2010 Feb 22.
This paper studies a class of new neural networks referred to as switched neutral-type neural networks (SNTNNs) with time-varying delays, which combines switched systems with a class of neutral-type neural networks. The less conservative robust stability criteria for SNTNNs with time-varying delays are proposed by using a new Lyapunov-Krasovskii functional and a novel series compensation (SC) technique. Based on the new functional, SNTNNs with fast-varying neutral-type delay (the derivative of delay is more than one) is first considered. The benefit brought by employing the SC technique is that some useful negative definite elements can be included in stability criteria, which are generally ignored in the estimation of the upper bound of derivative of Lyapunov-Krasovskii functional in literature. Furthermore, the criteria proposed in this paper are also effective and less conservative in switched recurrent neural networks which can be considered as special cases of SNTNNs. The simulation results based on several numerical examples demonstrate the effectiveness of the proposed criteria.
本文研究了一类新的神经网络，称为具有时变延迟的切换中立型神经网络（SNTNNs），它将切换系统与一类中立型神经网络相结合。通过使用一种新的Lyapunov-Krasovskii泛函和一种新颖的序列补偿（SC）技术，提出了具有时变延迟的SNTNNs的保守性较小的鲁棒稳定性判据。基于新的泛函，首先考虑具有快速变化中立型延迟（延迟的导数大于1）的SNTNNs。采用SC技术带来的好处是，一些有用的负定元素可以包含在稳定性判据中，而这些元素在文献中Lyapunov-Krasovskii泛函导数上界的估计中通常被忽略。此外，本文提出的判据在可视为SNTNNs特殊情况的切换递归神经网络中也是有效且保守性较小的。基于几个数值例子的仿真结果证明了所提判据的有效性。
