Abstract
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.
Original language | English |
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Pages (from-to) | 703-711 |
Number of pages | 9 |
Journal | Nonlinear Dynamics |
Volume | 60 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2010 Jun |
Externally published | Yes |
Keywords
- 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