${\mathcal H}_{\infty}$ WEIGHT LEARNING ALGORITHM OF RECURRENT NEURAL NETWORKS WITH TIME-DELAY
Choon Ki Ahn · Modern Physics Letters B · 2010
In this letter, we propose a new weight learning algorithm, called an [Formula: see text] learning law (HLL), for recurrent neural networks with time-delay. Based on the Lyapunov–Krasovskii stability theory, the HLL is presented to not only guarantee asymptotical stability but also reduce the effect of external disturbance to an [Formula: see text] 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.