Zhang Xian-Ming, Han Qing-Long
Centre for Intelligent and Networked Systems, School of Information and Communication Technology, Central Queensland University, Rockhampton QLD 4702, Australia.
IEEE Trans Neural Netw. 2011 Aug;22(8):1180-92. doi: 10.1109/TNN.2011.2147331. Epub 2011 Jun 23.
This paper is concerned with global asymptotic stability for a class of generalized neural networks (NNs) with interval time-varying delays, which include two classes of fundamental NNs, i.e., static neural networks (SNNs) and local field neural networks (LFNNs), as their special cases. Some novel delay-independent and delay-dependent stability criteria are derived. These stability criteria are applicable not only to SNNs but also to LFNNs. It is theoretically proven that these stability criteria are more effective than some existing ones either for SNNs or for LFNNs, which is confirmed by some numerical examples.
本文研究一类具有区间时变延迟的广义神经网络（NNs）的全局渐近稳定性，其中包括两类基本的神经网络，即静态神经网络（SNNs）和局部场神经网络（LFNNs）作为其特殊情况。推导了一些新颖的与延迟无关和与延迟相关的稳定性准则。这些稳定性准则不仅适用于SNNs，也适用于LFNNs。从理论上证明了这些稳定性准则对于SNNs或LFNNs而言比一些现有准则更有效，这一点得到了一些数值例子的证实。
