Trajectory tracking for delayed recurrent neural networks
Edgar Nelson Sanchez, José P. Pérez, José P. Pérez · 2006
This paper deals with the problem of trajectory tracking for delayed recurrent neural networks. The tracking error is global asymptotic stabilized by a control law derived on the basis of a Lyapunov-Krasovsky functional. Then, it is established that this control law minimizes a meaningful cost functional. Applicability of the approach is illustrated by means of an example.