An ℋ_∞ approach to stability analysis of switched Hopfield neural networks with time-delay

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.