A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay.
In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.
Mou
Shaoshuai
S
Department of ControlScience and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, China. shaoshuaimou@gmail.com
Gao
Huijun
H
Lam
James
J
Qiang
Wenyi
W
D000465:Algorithms; D006801:Humans; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors