title: Global asymptotic stability for a class of generalized neural networks with interval time-varying delays.
abstract: 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.
authors.affiliation: Centre for Intelligent and Networked Systems, School of Information and Communication Technology, Central Queensland University, Rockhampton QLD 4702, Australia. x.zhang@cqu.edu.au
authors.forename: Xian-Ming
authors.initials: XM
authors.lastname: Zhang
authors.forename: Qing-Long
authors.initials: QL
authors.lastname: Han
mesh_terms: D016571:Neural Networks, Computer; D013997:Time Factors