This paper proposes a new ℋ∞ weight learning law for switched Hopfield neural networks with time-delay under parametric uncertainty. For the first time, the ℋ∞ 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∞ norm constraint. An existence condition for the ℋ∞ 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.
Bibliographical noteFunding Information:
This paper was supported by Wonkwang University in 2010.
- Linear matrix inequality (LMI)
- Lyapunov-Krasovskii stability theory
- Switched Hopfield neural networks
- Weight learning law
- ℋ stability
ASJC Scopus subject areas
- Control and Systems Engineering
- Aerospace Engineering
- Ocean Engineering
- Mechanical Engineering
- Applied Mathematics
- Electrical and Electronic Engineering