期刊
NONLINEAR DYNAMICS
卷 60, 期 4, 页码 703-711出版社
SPRINGER
DOI: 10.1007/s11071-009-9625-6
关键词
H-infinity stability; Weight learning law; Switched Hopfield neural networks; Linear matrix inequality (LMI); Lyapunov-Krasovskii stability theory
资金
- Wonkwang University
This paper proposes a new H-infinity weight learning law for switched Hopfield neural networks with time-delay under parametric uncertainty. For the first time, the H-infinity weight learning law is presented to not only guarantee the asymptotical stability of switched Hopfield neural networks, but also reduce the effect of external disturbance to an H-infinity norm constraint. An existence condition for the H-infinity weight learning law of switched Hopfield neural networks is expressed in terms of strict linear matrix inequality (LMI). Finally, a numerical example is provided to illustrate our results.
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