H∞ weight learning algorithm of recurrent neural networks with time-delay

摘要

In this letter, we propose a new weight learning algorithm, called an H∞ learning law (HLL), for recurrent neural networks with time-delay. Based on the LyapunovKrasovskii stability theory, the HLL is presented to not only guarantee asymptotical stability but also reduce the effect of external disturbance to an H∞ norm constraint. An existence condition for the HLL is represented in terms of linear matrix inequality (LMI). An illustrative example is given to demonstrate the effectiveness of the proposed HLL.