The problem of the globally asymptotical stability of recurrent neural networks with time varying delay is investigated.
研究了带时变时滞的递归神经网络的全局渐近稳定性。
We give a simple sufficient condition on control gain that guarantees globally asymptotical stability of closed loop system.
本文提出了一个简单的关于控制增益的条件，保证了闭环系统的全局渐近稳定性。
It is proved that globally asymptotical stability and convergence of the resulting closed-loop system are guaranteed by the range of the prediction horizon.
从理论上证明了它可保证闭环系统的全局渐近稳定和收敛，并定量给出了预测水平的选择范围。
应用推荐